esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### XRD Analysis of Photothermal Sensitive Nanoparticles  
Fe\({}_{3}\)O\({}_{4}\) nanoparticles, known for their exceptional photothermal properties, often encounter issues of local aggregation when used in conjunction with bone tissue engineering scaffolds, leading to hindered cellular growth, differentiation, and even apoptosis. To address this, SiO\({}_{2}\)@Fe\({}_{3}\)O\({}_{4}\) nanoparticles were synthesized via TEOS hydrolysis-condensation, with the SiO\({}_{2}\) coating offering superior biocompatibility and stability, effectively mitigating Fe\({}_{3}\)O\({}_{4}\) particle aggregation. XRD analysis of both Fe\({}_{3}\)O\({}_{4}\) particles (S\({}_{0}\)) and SiO\({}_{2}\)@Fe\({}_{3}\)O\({}_{4}\) (S\({}_{1}\)) (as depicted in Figure 2) revealed diffraction peaks at 20 = 29.8\({}^{\circ}\), 35.1\({}^{\circ}\), 42.7\({}^{\circ}\), 52.9\({}^{\circ}\), 56.4\({}^{\circ}\), and 61.9\({}^{\circ}\), corresponding to (220), (311), (400), (422), (511), and (440), consistent with the standard data for Fe\({}_{3}\)O\({}_{4}\) powder diffraction. Additionally, a broad peak observed at 20 = 15\({}^{\circ}\)-25\({}^{\circ}\) in the S\({}_{1}\) curve may be attributed to amorphous SiO\({}_{2}\) particles, although  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number** & **PVA Conc. (\%)** & **HA: \(\beta\)-TCP (g:g)** & **HA: PCL (g:g)** & **Blank Column** & **Compressive Strength (MPa)** \\ \hline
1 & 8 & 9:1 & 4:1 & 1 & 10.823 \\
2 & 8 & 4:1 & 7:3 & 2 & 7.661 \\
3 & 8 & 7:3 & 3:2 & 3 & 7.881 \\
4 & 10.5 & 9:1 & 7:3 & 3 & 16.713 \\
5 & 10.5 & 4:1 & 3:2 & 1 & 16.239 \\
6 & 10  
further testing is necessary to validate the encapsulation of Fe\({}_{3}\)O\({}_{4}\) within SiO\({}_{2}\). Nevertheless, the relative intensity of the Fe\({}_{3}\)O\({}_{4}\) particle diffraction peaks in S\({}_{1}\) has decreased, suggesting successful encapsulation by SiO\({}_{2}\) and the formation of photothermal-sensitive nanoparticles SiO\({}_{2}\)@Fe\({}_{3}\)O\({}_{4}\).
## 3 CV QKD over Satellite Channels
### Quantum Channel and Protocol Parameters
Performance of CV QKD over short-range (terrestrial) free-space links can be essentially limited by quantum channel transmittance fluctuations [48; 49], also referred to as fading, which are mainly caused by the atmospheric turbulence effects of beam wander [50], when a beam spot travels around the receiving aperture. The channel fading then results in increase of the channel noise, detected on the receiver [48]. Even though this effect will be partially compensated for in the long-distance realization of CV QKD, where beam spot drastically expands during the propagation, which results in channel stabilization at the cost of increasing the overall loss [51] as well as by active beam tracking and stabilization systems, it must be taken into account in the realistic CV QKD security analysis. However, as the residual transmittance fluctuations due to atmospheric effects are slow (of the order of KHz [52]) compared to high achievable repetition rates of CV QKD systems (enabled by GHz rates of homodyne detectors [46]), the fading can be further compensated for by properly grouping the data according to estimated relatively stable transmittance values [53], which we also apply in our study.
## 5 Results
### Performance
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Mode** & **Thresh.** & **Hours** & **Precis.** & **Recall** & **Accur.** & **F2** \\ \hline \multirow{2}{*}{Single 5\%} & 0.25 & First 3 & **0.150** & **0.600** & **0.367** & **0.375** \\ \cline{2-6}  & & Last 3 & 0.167 & 1.000 & 0.167 & 0.500 \\ \cline{2-6}  & 0.5 & First 3 & **0.170** & **0.340** & **0.613** & **0.283** \\ \cline{2-6}  & & Last 3 & 0.620 & 0.155 & 0.373 & 0.182 \\ \cline{2-6}  & 1 & First 3 & & **0.000** & **0.833** & \\ \cline{2-6}  & & Last 3 & 0.129 & 0.260 & 0.583 & 0.216 \\ \hline \multirow{2}{*}{Single 10\%} & 0.25 & First 3 & **0.160** & **0.627** & **0.377** & **0.396** \\ \cline{2-6}  & & Last 3 & 0.163 & 1.000 & 0.163 & 0.494 \\ \cline{2-6}  & 0.5 & First 3 & **0.160** & **0.314** & **0.603** & **0.263** \\ \cline{2-6}  & & Last 3 & 0.155 & 0.633 & 0.377 & 0.391 \\ \cline{2-6}  & 1 & First 3 & & **0.000** & **0.830** & \\ \cline{2-6}  & & Last 3 & 0.152 & 0.327 & 0.593 & 0.266 \\ \hline \multirow{2}{*}{Single 25\%} & 0.25 & First 3 & **0.125** & **0.556** & **0.350** & **0.329** \\ \cline{2-6}  & & Last 3 & 0.183 & 1.000 & 0.183 & 0.529 \\ \cline{2-6}  & 0.5 & First 3 & **0.160** & **0.356** & **0.623** & **0.286** \\ \cline{2-6}  & & Last 3 & 0.190 & 0.691 & 0.403 & 0.452 \\ \hline \multirow{2}{*}{1} & \multirow{2}{*}{First 3} & \multirow{2}{*}{} & \multirow{2}{*}{} & \multirow{2}{*}{} & \multirow{2}{*}{} & \multirow{2}{*}{} \\  & & Last 3 & & & & \\ \cline{2-6}  & & Last 3 & 0.158 & 0.291 & 0.587 & 0.249 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: ARMA performance data for the “sum” term of ERGM model. Bold numbers are the highest scores.
# The Significance of Hydrophobicity for the Water Retention Properties of Sand and Coal
## 3 Results and Discussion
### Water Retention Curves of Sand and Sand Mixtures  
The WRCs of the natural sand and sand mixtures determined from inducing suction with a hanging column are presented in Figure 6, expressed in terms of the degree of saturation. The data in Figure 6 indicates that the natural sand and the sand mixtures all desaturated at low suctions (<1 kPa), and a reduction of the natural sand content (or an increase in the average CA) caused the degree of saturation at any suction to reduce. This observation is consistent with similar water retention curves for hydrophobic materials, which were expressed in terms of the gravimetric water content vs. suction [11].  
An insight into the differences between the wetting of the natural and hydrophobic sand at a granular scale is given in Figure 7, which shows the ESEM images of condensing water droplets and the men  
Figure 5: Advancing and receding angles measured for the sand and sand mixtures.  
Figure 6: Drying path of the water retention curves of different sand mixtures, expressed as the saturation degree vs. suction.  
It can be seen that the contact angles of the water droplets in Figure 7 were somewhat similar to those observed and measured in the sessile drop tests for the natural and hydrophobic sand (28\({}^{\circ}\) and 128\({}^{\circ}\), respectively). It is also interesting to note the difference in the number and size of the droplets between the hydrophilic surface (Figure 7a) and the hydrophobic one (Figure 7b). The menisci seen between particles in Figure 7 highlights the differing levels of hydrophobicity of the sands. Between the natural sand particles, a distinct curvature can be seen (arrow in Image a), whereas little curvature can be seen between the hydrophobic particles (arrow in Image b). Note that the droplets adjacent to the relatively straight meniscus appeared to have a contact angle of 90\({}^{\circ}\) or more (the upper particle in Figure 7b), reflecting strong local hydrophobicity. When considering the water held in the interparticle menisci or the funicular and pendular regime of the WRC [27], hydrophilic particles with curved menisci would contain more water than otherwise identical hydrophobic particles, which have relatively straight menisci. This is simply due to the curved geometry of the hydrophilic menisci requiring a greater volume than relatively straight menisci. This menisci phenomenon explains, to some degree, why more hydrophilic materials contain greater amounts of water at any suction in Figure 6.
## 8 Conclusions  
This paper offers a comprehensive survey of the models and algorithms used in the research on topic identification and trend prediction based on CNKI over the last 10 years and beyond, systematically summarized the overall state of the application of models and algorithms in topic identification and prediction analysis, and described the specific scenarios of application of the models and algorithms from the points of view of age distribution, type, number of categories, and analysis of specific applications. The main conclusions are as follows.  
Research on topic identification and prediction has become an important topic since 2019. More than half of the research has used one type of model or algorithm, while more than one-third of the research used two types of models and algorithms. The main themes of interest in this area are topic identification, topic prediction, technology prediction and patent analysis, including analysis methods such as LDA, Word2 Vec, K-means, VOLDA, PLDA and other approaches.  
In the application of topic recognition models and algorithms, the frequency of use can be listed from high to low as the LDA topic model and derived model, machine learning and deep learning, text mining-based methods and reference relationship-based analysis methods. Two kinds of methods are mainly used in trend prediction; one is deep learning or machine learning based on time series, the second is link prediction algorithms based on complex networks; the methods involved are summarized above.  
The characteristics of the indicators used in the sample literature are summarized, and a primary indicator system including five characteristic dimensions is proposed, namely, basic scalar, topic popularity and intensity, emerging degree, cross diffusion and network structure, covering 15 secondary indicators. The methods of validity verification for the topic recognition and prediction models are shown.  
In a word, models and algorithms based on machine learning and deep learning have become the most popular application. Internationally, these method has also been used widely to conduct data mining and text analysis in multiple subjects [90; 91]. The main challenges met in modeling and applying algorithms include accuracy, computability, reliability, viability and sustainability [92; 93].
## 6 Conclusions  
In this paper, CP is designed in a MLSCN with the CI method to transfer a large amount of spatial data under limited-resource conditions. Specifically, we have first built a system model to determine the limitations of CPD in the system. Then, this paper divides the contacts in the model into inter-layer contacts and intra-layer contacts, and performs CPD on the two types of contacts respectively according to the order in which the data is delivered in the network. For the inter-layer CP, we have proposed the CI algorithm named MPSO, which introduces the grid-based initialization method to maximize the differences between individuals in the initial group, and at the same time developed a quaternary optimization strategy to further improve the search ability of the algorithm. For the intra-layer contact plan, since each network layer plays a different role in MLSCN, we have designed different intra-layer CPs for different layers. A fair contact plan is designed for the UAV layer, and the HAP and satellite layers are designed with the corresponding CP with the shortest path. Finally, simulation results show that the proposed MPSO can outperform the other two classical CI algorithms.  
Figure 11: Comparison of average arrival time with the iteration times.  
Figure 10: Comparison of arrival rate with the iteration times.  
**Author Contributions:** Conceptualization, C.-Q.D. and L.G.; Data curation, C.-Q.D.; Formal analysis, C.-Q.D.; Investigation, C.-Q.D.; Methodology, C.-Q.D.; Software, C.-Q.D.; Supervision, Q.S. and L.G.; Validation, L.G.; Writing--original draft, C.-Q.D.; Writing--review & editing, Q.S.  
**Funding:** This research work was jointly supported by the National Natural Science Foundation in China (61601075, 61671092, 61771120, 61801105) and the Natural Science Foundation Project of CQ CSTC (cstc2016 cyjA0174).  
**Acknowledgments:** The authors are deeply grateful for all help from the references quoted and materials used for our experiments, and also would like to give gratitude to the reviewers for their constructive comments.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## Appendix A MRI Protocol  
MRI examinations [58] were performed with a 1.5-T scanner (Achieva, Philips Healthcare) or a 3.0-T MR scanner (Magnetom Tim Tio, Siemens Healthineers, Germany; or Ingenia, Philips Medical Systems, the Netherlands). For bowel preparation, 20 mg of scopolamine butylbromide (Buscopan; Boehringer Ingelheim) was injected intramuscularly, and sonography transmission gel (50-100 mL) was administered in the rectal lumen for the mass at the lower or middle rectum before MRI scanning.  
The MRI sequences included high-resolution T2-weighted images using a respiratory-triggered fast spin echo (axial, sagittal, and oblique axial and coronal orientations), axial T1-weighted images, axial diffusion-weighted images using single-shot echo-planar imaging (the highest b-values 1000 s/mm\({}^{2}\)), as well as gadolinium contrast enhanced T1 weighted images using a three-dimensional gradient-echo sequence. The oblique T2-weighted image sequence was obtained orthogonal or parallel to the long axis of the tumor. An intravenous bolus of gadobutrol (Gadovist; Bayer AG, Berlin, German: 0.1 mL/kg of body weight) or gadopentate dimeglumine (Magnevist; Bayer Healthcare, Berlin, Germany: 0.2 mL/kg of body weight) was injected at a rate of 2.0 mL/s. The details on MRI sequences are summarized in Table 11.  
The effectiveness of T2 and ADC in staging/restaging rectal cancer has been widely accepted. Regarding DCE, however, a consensus meeting of 14 abdominal imaging experts from the European Society of Gastrointestinal and Abdominal Radiology (ESGAR) recommended that, although some promising data are available, it should currently be considered as a research tool and not be adopted routinely [59]. Therefore, we acquired contrast enhanced T1 weighted gradient echo images but not DCE.
## 3 Discussion
### Metabolite Identification and Biological Significance
#### 3.2.2 Positive Mode
PsA patients have been found to exhibit increased amounts of degradation products of collagen in the serum that may originate from cartilage destruction or skin lesions, and high collagen turnover is suspected in the skin lesions of psoriasis patients [34]. In addition, as shown in Table 6, proline is a metabolite that could be associated with high disease activity. Given that the PASI was used to classify disease severity, it is likely that proline concentration is abnormal due to high collagen turnover in the skin lesions, or joint cartilage of high-disease-activity-PsA patients. These models also identified diacylglycerolcords (DAGs) and monoacylglycerolcords (MAGs) as contributors to disease severity. Although information about the role of the specific DAGs and MAGs tentatively identified is very sparse in the literature, the compound classes are involved in immune signaling, and regulation. DAGs can act as secondary messengers and drive the activation, proliferation, and effector functions of several innate and adaptive immune cells [35]. DAGs can indirectly activate the nuclear factor kappa light chain enhancer of activated B cells (NF-KB) and extracellular regulated kinase (ERK) pathways of T cells, B cells, macrophages, and more [35]. MAGs can activate immune receptors including PPARs that regulate lipid metabolism and control energy production in the mitochondria, inflammation, cytokine secretion, and more [36]. Many other tentatively identified metabolites in positive mode belong to lipid and fatty acid compound classes, such as medium- and long-chain fatty acids. In general, these compound classes are noted to be involved in inflammation and several diseases with similar manifestations to PsA. Multiple long- and medium-chain fatty acids have been found to be significantly different between PsA and healthy patients [37]. The roles of fatty acids include being precursors to pro- and anti-inflammatory eicosanoids, and regulating immune homeostasis via altering cytokine production, leukocyte recruitment, and more [30; 37; 38]. Overall, the results highlight dyslipidemia among PsA patients, and that lipids and fatty acids can play roles in immune-mediated inflammation and PsA pathogenesis.
## 7 Conclusions  
This review presents a discussion of current sample preparation methods for the analysis of food contaminants and residues by GC-MS/MS. The main criteria to consider on preparation methods for VOCs analysis, HS extraction, and SPME methods are sensitivity and easiness of quantitation of target analytes. Moreover, GC-MS/MS is gradually being preferred to GC-MS for its higher sensitivity and better separation rather than preparation selectivity. Thus, for the analysis of more SVOCs or thermally stable compounds, it might be advisable to develop methods for the analysis of multiple contaminants of different classes considering a single sample preparation technique and preferably one chromatographic run. Multi-residue preparation methods, such as QuEChERs and adsorption extraction coupled with SBSE and SDME, are the most important technologies currently available, enabling the extraction of as many targets as possible simultaneously. Due to the complexity of certain food matrices, the use of separation, clean-up, and derivatization processes might be useful to increase the extraction rate and reduce the detection limit. In addition, we also proposed guidelines for determining whether ME might occur and interfere with the results; matrix-matched calibration is one of the most popular methods for the multi-residue analysis of agricultural products. Moreover, a discussion of emerging contaminants that may threaten human health and the environment in the future is provided. QuEChERs might be employed to detect these emerging contaminants. In conclusion, a comprehensive strategy is required for sample preparation for the analysis of food contaminants and residues by GC-MS/MS in order to effectively achieve food safety.  
Conceptualization, M.-L.X.; writing--original draft preparation, M.-L.X. and Y.G.; writing--review and editing, X.X.H. and X.W.; funding acquisition, B.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of the People's Republic of China, grant number 22011540378, 21773080, 21773079, the Development Program of the Science and Technology of the Jilin Province, grant number 20190201215 jc, 2020040193 YY. The APC was funded by 22011540378.  
The authors declare no conflict of interest.
# Theoretical Study on Prestress Loss in Cross-Tensioned Concrete Pavement with BFRP Tendons
## 4 Comparison of the Prestress Loss with Steel Strands
### Calculation Process of the Prestress Loss for Steel Strands
#### 4.1.3. Prestress Loss Due to Elastic Compression of Concrete  
Like the case of BFRP tendons, the prestress loss caused by elastic compression of concrete can be neglected given the application of the hypertension in cross-tensioned concrete pavements.
# Detection of Targets in Road Scene Images Enhanced Using Conditional GAN-Based Dehazing Model
## 3 Image Dehazing Model
### Discriminator  
Figure 4 shows the discriminator of our proposed image dehazing model. It consists of four layers of strided convolution. The inputs were the hazy image concatenated with  
Figure 3: Progression of generator output—clear image and generated images at various stages of prediction.  
Figure 2: Our proposed generator network.  
the clear image and the generated fake image. The activation function at the output layer was sigmoid. The discriminator performed a patch-wise comparison of the clear image with the generated fake image to determine whether the generator output was real or fake. Therefore, the discriminator forced the generator to improve the realness of the output and helped remove the artifacts in the hazy image. In our experimentation, we had two versions of discriminator. The first one (D1) was the same as the base model with the use of LeakyReLU as the activation function. The second one (D2) utilized Mish as the activation function.
## 4 Arsenic Removal Processes
### Membrane Processes
#### 4.3.1 Arsenic Removal by Membrane Distillation  
As a non-isothermal membrane separation process, membrane distillation (MD) utilizes a microporous hydrophobic membrane with pore size ranging from 0.01 \(\mu\)m to 1 \(\mu\)m [97]. For effective operation, only vapor and non-condensable gases must be present within the membrane pores and the membrane must not be wet [97; 98]. Commercially available hydrophobic micro-porous membranes include polytetrafluoroethylene, polyethylene, polypropylene and polyvinylidenefluoride membranes [98]. The simplest and most economical and efficient of the different kinds of MD is direct contact membrane distillation (DCMD) [97; 98]. DCMD directly separates the hot feed and the cold permeate with the aid of the membrane. Up to 100% of arsenic has been removed from contaminated groundwater with DCMD [97; 98].
###### Abstract  
We proposed a narrowband perfect absorber that was based on dielectric-metal metasurface for wide-band surface-enhanced infrared sensing. It is found that the narrowband perfect absorber can generate the hybrid guided modes with high quality-factor at infrared frequencies, which make the absorber highly sensitive to the surrounded analyte. Moreover, tuning the incident angle can actively modulate the resonant wavelength of absorber. Such an absorber with excellent features is employed to realize both refractive index sensing and infrared vibrational fingerprint sensing on a single substrate. It is demonstrated that a refractive index sensitivity of 1800 nm/RIU and figure of merit of 62 RIU\({}^{-1}\) can be obtained as the refractive index sensor. While, as a surface enhanced infrared absorption spectroscopy substrate, two closed vibrational modes of analyte with nanometer thick layers can be effectively identified and selectively detected with 50-folds enhancement by actively tuning the incident angle without any change in the structural parameters (periodicity, width, height, and refractive index of the grating) of the device after fabricating. Our study offers a promising approach for designing high-performance surface-enhanced infrared optical sensors in the infrared region.  
surface-enhanced infrared sensing; narrowband perfect absorber; dielectric-metal metasurface +
Footnote †: journal: Applied Physics Letters  
18 February 2020; Accepted: 19 March 2020; Published: 27 March 2020
## 3 DDoS Attack Detection Methods
### DDoS Attack Detection Studies Using Anomaly-Based Methods  
DDoS attack detection methods based on anomaly approaches are classified under the following: detection based on entropy, chaos theory approach, queuing modelling approach, statistical approach, heuristic-based detection, machine learning, and deep learning approaches, as illustrated in Figure 8. In this section, these methods are discussed. As well, research studies in each case are presented.
## 3 Related Works on Models Compression  
The idea of compressing models has originated from the observation that many of the parameters used in deep neural networks (DNNs) are redundant [31] and thus may be removed without loss of performance. In addition, using a very deep model (in terms of number of layers) is not always necessary, and it may be possible to eliminate some of the hidden layers without considerably decreasing the accuracy [32]. As a consequence of the compression, the complexity will be reduced [9], which makes the application suitable for low resources.