esa-sceva/satcom-chunk-collection huggingface dataset:
# SQRT: A Secure Querying Scheme of Routing Table Based on Oblivious Transfer
###### Abstract  
The user equipment and directory server stay in the state of asymmetric information in anonymous networks, so that it is hard to coordinate information leakage prevention and information sharing when we explore the routing addressing technology. The severe security risk faced by existing anonymous networks, such as Tor and I2P, is the leakage of privacy information of routing nodes. This paper tries to resolve this problem and proposes a novel secure querying scheme of the routing table (SQRT) based on oblivious transfer, which can effectively ensure that both user equipment and directory server faithfully follow the routing querying protocol and protect the privacy information of both parties to the greatest extent. The SQRT scheme can realize that the directory server cannot only send the required routing nodes to the user equipment, but can also ensure that the directory server cannot know the exact routing nodes received by the user equipment and protect the information of other routing nodes in the directory server from disclosure. Security analysis shows that in the scenario where the directory server and user equipment are semi-honest, respectively, the SQRT scheme can ensure the privacy of both directory server and user equipment. The experimental simulation results show that compared with existing schemes, the SQRT scheme has obvious performance advantages in terms of the degree of anonymity, protocol running time, and communication traffic.  
secure querying; routing table; oblivious transfer; anonymous networks; security analysis +
Footnote †: journal:  
Article
# Sparse Representing Denoising of Hyperspectral Data for Water Color Remote Sensing
## 4 Discussion  
### Influence of \(K\)  
Sparsity is an important parameter in our proposed algorithm. It decides how many atoms are involved in compositing the \(R_{\mathrm{rs}}\) signal. More atoms mean more spectral details are included in the result. Simultaneously, more errors might also be retained. Therefore, we evaluated the performance of the proposed algorithm under different sparsity (\(\mathbf{k}\)), to give suggestions for the denoising signals at different SNR levels. In the same way as the former sections, all of the calculations were carried out using the simulated dataset at three input SNR levels. To exhibit the results more clearly, we further calculated the SNR difference before and after the denoising. A SNR difference equal to 0 means the denoising made no contribution to the recovery of useful signals. An ideal SNR difference should be larger than 0, which means the denoising is effective. On the contrary, if the SNR difference is less than 0, the denoising destroyed part of the useful signals. The dashed line in Figure 9 represents the SNR difference equals 0.  
Figure 8: Typical PRISMA and their denoised spectra.  
Figure 9: SNR difference of the proposed algorithm under different \(k\) value.  
The violin plot of the SNR differences under three input SNR levels is shown in Figure 9. With the increase in k, the denoising performance under different SNR levels exhibited similar trends. When k was less than 10, the SNR differences increased synchronously with k. After k was larger than 10, the SNR differences became flat. This suggests that the SR method is not stable under low sparsity conditions. Another evidence of the stability is the width of the violins in Figure 9a,b. A narrower and longer violin indicates a wider SNR difference distribution. A stable denoising of one dataset is expected to yield a concentrated performance. From this aspect, when effective denoising is generated (Figure 9a,b), a higher sparsity means the model is more stable.  
What is interesting is that when the input noise is strong (Figure 9a), the increase in the sparsity will not always lead to a better performance. The SNRs slowly decreased when sparsity was larger than 15, even though they are still generally higher than the dashed line. This denotes that, at highly noisy levels, a larger k value of SR method will keep more of the noise signal. Sparse representation cannot completely separate the noise signal.  
Considering both the high and low SNR levels, an optimized sparsity in this research was set to 15. This is also the parameter that was applied in the previous sections. In practice, when given enough prior information, we suggest using a lower sparsity (k = 10) for the low SNR signals (SNR \(\approx\) 30), and a larger one (k = 30) for the high SNR (SNR \(\approx\) 70) signals.
# Sericin for Tissue Engineering
## 3 Sericin and Tissue Engineering
### Sericin for Bone Regeneration  
Self-healing in severe bone defects is rare, necessitating treatment with artificial bone substitutes [107]. Sericin extracted from silk cocoons has been used in soft tissue engineering, for example, for epithelial and connective tissue repair [32]. The mechanical properties of sericin are suitable for soft tissue repair; however, the lack of mechanical strength makes sericin less applicable for bone regeneration [60; 107]. Nevertheless, sericin can be used as an artificial bone substitute for severe bone defects if it is modified to improve its mechanical and bone-inducing properties [107].  
Pure sericin is not used as a scaffold for bone tissue engineering because of its poor mechanical properties [60]. The nucleation of hydroxyapatite (HA) crystals is caused by anionic side chains that bind Ca2+, which means that the amount of acidic amino acids in sericin may affect the nucleation of hydroxyapatite crystals [108]. [PERSON] et al. reported that HA/sericin composite films could maintain the viability of human osteosarcoma MG-63 cells, owing to the deposition of HA and its three-dimensional structure; hence, these films could be applied for bone tissue engineering [109]. [PERSON] et al. reported the non-toxicity of HA/sericin nanocomposites, which increased cell viability [110]. [PERSON] et al. fabricated a sericin methacyloyl/graphene oxide hydrogel with tunable mechanical strength and osteoinductive ability as a scaffold for the functional repair of bone tissue [107]. This scaffold had good biocompatibility, cell adhesion properties, cell proliferation and migration abilities, and osteogenic induction properties [107]. After implantation into a rat model of calvarial defect, this scaffold effectively promoted new bone regeneration and induced autologous bone marrow-derived  
Figure 7: Commercially available oral gargle containing silk proteins.  
mesenchymal stem cell differentiation, thereby achieving structural and functional repair within 12 weeks [107]. Therefore, sericin-based biomaterials can be used in bone tissue engineering.
# Nucleobase-Containing Polymers: Structure, Synthesis, and Applications
## 3 Synthetic Nucleobase-Containing Polymers
### Synthesizing Nucleobase Polymers by NMP  
As we described before, the coordination of nucleobase (guanine and cytosine) with a metal catalyst (e.g., CuBr) usually attenuates the polymerization kinetics of ATRP. As such, nitroxide-mediated polymerization (NMP) could be utilized as another controlled radical polymerization (CRP) technique to produce nucleobase-containing polymers [52; 53]. [PERSON] et al. utilized a novel difunctional alkoxyamine initiator to polymerize sytrenic nucleobase monomers with poly(\(n\)-butyl acrylate) homopolymers via a two-step NMP route [33] (Figure 3). The resulting triblock copolymers contain either adenine or thymine as outer blocks, and \(n\)-butyl acrylate rubber as inner blocks. Once adenine and thymine polymers are mixed in chloroform, the viscosity of the blend increased dramatically, indicating the existence of hydrogen-bonding interactions between complementary nucleobase subunits since viscosity is a function of effective molecular weight. Furthermore, the annealed mixture in the solid state revealed a higher softening temperature and presented further proof of a complementary A-T hard phase. Although the NMP method avoids using a metal catalyst in polymerization, it still has some inherent challenges that limit its wide application in preparing nucleobase-containing polymers. For example, the slow polymerization kinetics usually requires high temperatures and a long polymerization time.
## 2 Materials and Methods
### Defining NDVI- and Phase-Space-Based Rice Phenometrics  
The active tillering date (\(D_{til}\)), middle heading date (\(D_{head}\)), and maturity date (\(D_{mat}\)) were first defined for the NDVI time series: The date of the maximum point on the first derivative was used to estimate \(D_{til}\); the date of the maximum point was used to estimate \(D_{head}\); and the date of the minimum point on the first derivative was used to estimate \(D_{mat}\) (Figure 4a) [24; 45; 46]. Then, we applied these existing phenological extraction methods to the phase-space. Specifically, the phenometrics were redefined based on the time series of dist: \(D_{til}\) was redefined as the date of the maximum point on the first derivative of _dist_; \(D_{head}\) was redefined as the date of the maximum value of _dist_ and \(D_{mat}\) was redefined as the date of the minimum point on the first derivative of _dist_ (Figure 4b).  
Since soil-related factors (e.g., soil moisture) greatly impact the VI values in the early growth period of rice (i.e., from transplanting to active tillering) due to the low vegetation cover, we prefer to set the growth season of interest to start at active tillering instead of transplanting. As the heading stage was supposed to divide rice development into vegetative and reproductive growth [45; 47; 48], we defined the vegetative phase as the period from \(D_{til}\) to \(D_{head}\) and the reproductive phase as the period from \(D_{head}\) to \(D_{mat}\). Thus, another three phenometrics, growth season length (\(L_{season}\)), vegetative phase length (\(L_{veg}\)), and reproductive phase length (\(L_{rep}\)), were calculated as follows:  
\[L_{season}=D_{mat}-D_{til}, \tag{6}\]  
\[L_{veg}=D_{head}-D_{til}, \tag{7}\]  
Figure 4: Temporal profiles and the first derivatives of NDVI (**a**) and _dist_ (**b**). The vertical dashed lines refer to the dates of active tillering (\(D_{til}\)), middle heading (\(D_{head}\)), and maturity (\(D_{mat}\)), from left to right.  
\[L_{rep}=D_{mat}-D_{head}, \tag{8}\]  
where \(L_{season}\), \(L_{vcg}\), and \(L_{rep}\) represent the lengths of growth season, vegetative phase, and reproductive phase (d), respectively. Particularly, the NDVI-based \(L_{season}\), \(L_{vcg}\), and \(L_{rep}\) were calculated by the NDVI-based \(D_{til}\), \(D_{head}\), and \(D_{mat}\), while the phase-space-based \(L_{season}\), \(L_{vcg}\), and \(L_{rep}\) were calculated by the phase-space-based \(D_{til}\), \(D_{head}\), and \(D_{mat}\).
## 2 Data
### Agro-Meteorological Data  
Agro-meteorological data for the period 2000-2019 at a deg resolution of 0.25 \(\times\) 0.25 were derived from the ERA-5 reanalysis generated by the European Centre for Medium-Range Weather Forecasts and freely distributed through the Copernicus Climate Data Store. They included hourly data at surface level consisting of: 2-m air temperature, total precipitation, surface incoming solar radiation, and volumetric soil water at 0-7-cm and 7-28-cm depths. These parameters were aggregated into daily means and/or sums using Climate Data Operators (CDO) software [29]. Additionally, minimum and maximum daily air temperatures were calculated.
# Prediction of Deflection of Shear-Critical RC Beams Using Compatibility-Aided Truss Model
## 2 Calculating Method of Deflection of Shear-Critical RC Beams  
Figure 1 shows an RC beam subjected to a concentrated load. The load acting on the beam is transferred from the loading point to the support by flexure and shear, and the total deformation of the beam is represented by the sum of the deformations due to flexure and shear. Deflection, which is a type of deformation, can be expressed by the following equation:  
\[\Delta_{tot.}=\Delta_{f}+\Delta_{s} \tag{1}\]  
After the shear-critical RC beam reaches the peak load, as shown in Figure 1, the deflection increases, but the load decreases. This post-peak behavior is the result of a sudden collapse in the shear-resistance mechanism of the shear-critical RC beam. In this step, shear deformation is dominant rather than flexural one. Therefore, it is noted that  
Figure 1: Flexural and shear deflections of a RC beam.  
after the peak load, the principle of superposition in Equation (1) cannot be applied, and only the shear deflection should be considered.  
The flexural deflection, \(\Delta_{f}\), in Equation (1) can be calculated from the curvature of the section; it shows a different behavior from the elastic theory due to cracking, which is a characteristics of concrete structures. The flexural deflection of cracked RC beams can be easily obtained using the following [PERSON]'s equation recommended by ACI 318-19 [PERSON] (1986).  
\[I_{e}=\frac{I_{cr}}{1-\left(\frac{(2/3)M_{e}}{M_{a}}\right)^{2}\left(1-\frac{I _{cr}}{I_{g}}\right)} \tag{2}\]  
Equation (2) can be used to calculate the deflection of RC beams using the elastic deflection equation. For 3- and 4-point loading, the deflection of RC beams can be obtained from the following formula:  
\[\Delta_{f}=\frac{M_{a}}{24E_{c}I_{e}}(3I^{2}-4a^{2}) \tag{3}\]  
Figure 1 shows the behavior of typical flexure- and shear-critical RC beams. As shown in Figure 1, unlike flexure-critical RC beams, shear-critical RC beams exhibit nonlinear behavior after shear deterioration, resulting in greater deflection for the same load than flexure-critical RC beams. Equation (3) is an elastic deflection equation that does not consider shear deflection. Therefore, as shown in Figure 1, when deflection is additionally increased owing to shear deterioration, a shear analytical model that can reasonably consider shear deformation is required. In this study, the average shear strain of an RC beam was calculated using the TATM developed by the author [PERSON] (1986). The theoretical background and calculation methods of the TATM are described in detail in Section 3. As shown in Figure 1, the average shear strain \(\gamma_{It}\) obtained using the TATM is used to obtain the shear deflection from the relationship with the shear span as follows:  
\[\Delta_{s}=a\;\gamma_{It} \tag{4}\]
## 3 New Techniques Used in Detection of Forest Fire in China
### Auto-Identification of Forest Fire Hot Spots  
The traditional approach to identify forest fire hot spots often involves visual interpretation of satellite images. There is a need to develop techniques for auto-identifying forest fires spots, so that fires can be detected and managed in timely manner. The artificial neural network (ANN) theory with Back Propagation (BP) learning algorithm provides a new means to solve this problem [60]. [PERSON] _et al._ developed an ANN method to detect forest fire spots in Hubei Province of China using 43 meteorological satellite images [61]. Out of these images, 31 were used for collecting training samples and the rest were used for assigning testing samples. The results showed that the identification of the fire spot was related to seven characteristics associated with the radiation values of the five channels of the NOAA/ AVHRR, the land cover types, and the differences of contiguous BT. The ANN method can capture the major characteristics of fire spots and non-fire spots, and has an ability to automatically identify fire spots from the satellite images [61]. Compared to the visual interpretation, the ANN method can identify fire hot spot from the satellite images with a similar accuracy [60-63]. [PERSON] _et al._[64] also used MODIS imagery and ANN method to predict forest fire in the Brazilian Amazon. The result showed that the spatial distribution of the areas with fire risk were consistent with the fire events observed. The ANN model allowed a fast and relatively precise method to predict forest fire events in the studied area [64].
## 2 Materials and Methods
### Test Material
#### 2.1.2 Test Soil and Crops  
The test soil was collected from the surface layer of agricultural planting land in Xinxiang City, Henan Province, and the sampling depth was about 5-20 cm. The collected soil samples were brought back to the laboratory to initially remove large particles of stones and crop residues in the soil samples, placed in a ventilated place to dry naturally, mashed and ground, and finally passed through a 20-mesh sieve and mixed well for later use. The soil tested in this experiment was a weakly alkaline semi-leading soil (cinnamon soil), with a pH of 7.69, cation exchange capacity (CEC) of 15.01 cmol kg\({}^{-1}\), conductivity (EC) and organic matter content (OM) of 725.33 \(\mu\)S-cm\({}^{-1}\) and 2.28 g.kg\({}^{-1}\) and total nitrogen (TN) and total phosphorus (TP) of 0.85 mg\(\cdot\)kg\({}^{-1}\) and 0.80 mg\(\cdot\)kg\({}^{-1}\), respectively [15]. Referring to the determination of total mercury, total arsenic and total lead in soil quality, the atomic fluorescence method (GB/T22105.2-2008), part 2: determination method of total arsenic in soil [16], could find that the arsenic content of the test soil was 16.4 mg\(\cdot\)kg\({}^{-1}\) (Table 1).  
The test crops were _Brassica rapa_ var. _paradinensis_ cv. Shanghaiqing and Sijiucaixin. Shanghaiqing _Brassica rapa_ was purchased from Xinxiang Jinwangjie Seed Industry Co., Ltd. (Xinxiang, China), Sijiucaixin _Brassica rapa_ was purchased from Shenyang Boster Agricultural Technology Co., Ltd. (Shenyang, China), and both crops were seasonal varieties and could grow normally in the test soil.
## 4 Mapping YANG to OPC UA
### YANG Module Mapping  
The process of mapping YANG to OPC UA model design begins with the YANG module statement, which acts as the highest level of the YANG data model hierarchy. Each YANG module is then correlated to the model design in OPC UA, and the mapping process involves associating various elements of the module. Table 2 summarizes the mapping with reference to the Appendix A Section for a detailed visual representation of the mappings. The module name from YANG is mapped to the file name in the OPC UA model design. The namespace from the YANG model is mapped to the default XML namespace, the target namespace, and the target XML namespace in the model design XML file. The prefix statement from YANG is mapped to the namespace element prefix and the XML prefix attribute. For every import and included statements in YANG, the OPC UA model design XML file shall contain a namespace element with a prefix, an XML prefix, and a namespace URL. The organization statement in YANG specifies the name of the organization responsible for this module, while the contact statement provides contact information for the module, including the name, postal address, telephone number, and email address. Because these statements cannot be directly mapped to any OPC UA model design elements, a custom object type named module info is created. An object instance of this type is then mapped with these statement values. The revision statement is an essential part of the YANG schema providing detailed revision information and dates related to the module's editorial history. However, it cannot be directly mapped to any OPC UA model design elements. To address this, a custom object type called revision info is created, and an object instance of this type is mapped to the revision statement values.
## 4 Experimental Methodology and Measurement Results
### RRU Synchronism
#### 4.2.2 Spectrum Measurement  
To measure the spectrum of the asynchronous phase noise, a 5 MHz sine wave modulated at the carrier frequency (3.6864 GHz), i.e., a 3.6914 GHz sine wave, was connected to one of the RF-in connectors of both RRUs with cables as illustrated in Figure 9.  
The DU provided PRBSs to keep the CDRs at the RRUs locked and received one sine wave from each RRU, sampled at 81.92 MSps; the sequences are about 25 ms long. A phase shift was applied to one sine wave and this phase-shifted sine wave was used to cancel the other sine wave. The spectrum of the cancellation result can be viewed as the asynchronous phase noise spectrum.  
Figure 10 includes the results of two test scenarios, back-to-back and with a 100 m DL fiber. There is little noticeable difference between the two results. If the received sine  
Figure 8: Jitter measurement setup and results. The results are shifted horizontally to align the edges of RRU2’s clock (labeled in pink) for easy comparison. (RTO: real-time oscilloscope.)  
Figure 9: Measurement setup.  
wave spectra of both scenarios are plotted together, the back-to-back case (Figure 10a) has slightly less phase noise.  
About 34 dB suppression was reached for both scenarios. For each scenario, if we compare the two spectra, we can see that the phase noise contained in the two sine waves is mainly correlated. The correlated phase noise originated from the sine wave generator and the retrieved 30.72 MHz clocks, which were extracted from the DL bitstreams generated with one common reference clock at the DU. The spikes at 5 MHz \(\pm\) 500 KHz were caused by the power supplies of the AAUs.  
A similar figure has been published in [15]. The results are slightly different because the loop filters of the PLLs have been adjusted for better stability.
## 3 Results and Discussion  
### Optimization of the HS-SPME Methods  
Compared with 1-D GC, 2-D GC provides improved analyte peak capacity, along with reducing the problem that chromatographic peaks are masked by the matrix. Figure 2 illustrates a two-dimensional contour plot obtained for the Guotai sample. The compounds displayed in this figure could not have been separated using conventional 1-D-GC methods, especially some trace compounds that may be masked by high-content compounds. To detect trace compounds in Baijiu comprehensively, the parameters of the HS-SPME method need to be optimized. This was achieved using 119 representative trace compounds naturally present in Baijiu (rather than the model synthetic solution, spiked with standards). These representative trace compounds belonged to quite different chemical classes, which also had an important effect on the aroma of Baijiu.  
Among the parameters affecting the extraction efficiency, most were set to the same values in the various SPME methods published. For example, it is common to saturate with sodium chloride (NaCl) to promote aroma release and use magnetic stirring [26]. However, for a complex matrix such as Baijiu and a stronger response of the combined detector, some critical parameters, such as sample dilution, sample volume, extraction time, and extraction temperature, needed to be re-optimized. A compromise solution of SPME optimization should always be taken into consideration and each experiment was performed under the best optimization parameters for the previous experiment. The evaluation index of the optimization results is not the total peak area of all compounds, but they are classified and compared to avoid the wrong choice of optimal conditions because the peak area of a certain type of compound is too large.