esa-sceva/satcom-chunk-collection huggingface dataset:
# IPv6 Convergence for IoT Cyber-Physical Systems
## 2 Background
### The Path Towards IP Convergence  
The potentially many different permutations of IP and non-IP network infrastructure types coupled with requirements to support a multitude of services necessitate a design where IP to non-IP translation and interconnection does not require dedicated translation nodes and in fact, can be dynamically triggered on demand. Dynamic interconnection is valuable for dealing with volatile IoT infrastructure where nodes can both join and leave networks and may be mobile. We show an example of a hetnet infrastructure in Figure 5, where IP to non-IP interconnectivity is required at multiple stages and where a plurality of services require support.  
Our research focused on the extension of IP support to communications technologies where native IP support was not currently available. We target LoRAWN in particular. Existing academic research regarding IPv6 over LPWA is very limited. In 2017, [PERSON] et al. proposed doing this by replacing the LoRaWAN MAC protocol with the Contiki networking stack [33]. In 2016, [PERSON] et al. proposed an approach analogous to the standardized and established 6 LoWPAN protocol where IPv6 is adapted via header compression and field eliding to IEEE 802.15.4. In this case, they proposed adopting a similar approach for LoRaWAN; a scheme they named 6 LoRaWAN [34]. However, our proposed approach was extended to reduce IPv6/6 LoWPAN overhead even further and generalised to extend over additional communication technologies.
# Design of a Land Area Measuring Instrument Based on an STM32 and a BeiDou Positioning Chip
## 5 Measurement and Analysis
### Function and Precision Test
#### 5.2.3 Downtown Area  
Finally, we selected Deji Square in Xinjiekou, the busiest area in Nanjing, as a typical downtown test area in Figure 13. The underground part of the square is connected to the subway station, the traffic is dense, and the electromagnetic interference is strong. The area is characterized by dense high-rise buildings, with an average building height of more than 200 m and a minimum building spacing of less than 15 m, forming a complex urban canyon environment. During the test, the number of visible BeiDou satellites was reduced to 5-8 (the GPS was 4-6 in the same period), the average carrier-to-noise ratio was 6.2 dB-Hz lower than that in the open area, and the multipath effect was significantly enhanced. The area data of five repeated measurements are shown in Table 6. The comparison between the actual trajectory and the test trajectory is shown in Figure 14. The maximum relative error of 2.72% verifies the reliability of the equipment in this signal attenuation environment.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Date** & **Weather** & **Measuring** & **Actual Area** & **Relative Error** \\  & **Condition** & **Area (m\({}^{2}\))** & **(m\({}^{2}\))** & **(\%)** \\ \hline
13 March & Cloudy & 85,714 & & 0.84 \\
15 March & Light rain & 87,309 & & 2.72 \\
17 March & Sunny & 85,918 & 85,000 & 1.08 \\
19 March & Cloudy & 86,823 & & 2.14 \\
21 March & Sunny & 86,504 & & 1.77 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Deji Square area test results.  
Figure 11: The actual stepping point trajectory around the dormitory building. (**a**) Actual field scene (sunny); (**b**) Schematic satellite view of the dormitary building area. Green line in-dicates the walking path during sampling.  
Figure 12: Dormitory building area test trajectory.
# Study on the Wellbore Flow for Carbon Dioxide Fracturing
## Appendix B Thermal Resistance of Wellbore  
In order to ensure the sand-carrying and fracturing scale, annulus synchronously injecting is the most used injection mode for CO\({}_{2}\) fracturing.  
The expressions of thermal resistance between the stratum and annulus fluid, and between the annulus and tubing fluid, are as follows, respectively,  
\[\text{R}_{\alpha t}=\frac{T_{D}}{2\pi\lambda_{e}}+\left(\frac{1}{2\pi\lambda_ {\alpha t}}\ln\frac{d_{\alpha o}}{d_{ci}}+\frac{1}{2\pi\lambda_{ce}}\ln\frac{ d_{b}}{d_{\alpha o}}+\cdots\right)+\frac{1}{h_{\alpha o}\pi d_{ci}} \tag{2}\]  
\[R_{at}=\frac{1}{h_{ti}\pi d_{ti}}+\frac{1}{2\pi\lambda_{t}}\ln\frac{d_{to}}{d _{ti}}+\frac{1}{h_{to}\pi d_{io}} \tag{3}\]  
where \(T_{D}\) is dimensionless temperature, for characterizing the formation temperature distribution during the circulation; \(\lambda_{e}\) is the heat conductivity of formation, W/(m\(\cdot\)K); \(h_{to}\) is the heat convection coefficient of the outer wall in the annulus, W/(m\({}^{2}\cdot\)K); \(h_{ti}\) is the heat convection coefficient of the inner wall of tubing, W/(m\({}^{2}\cdot\)K); \(h_{to}\) is the heat convection coefficient of the outer wall of tubing, W/(m\({}^{2}\cdot\)K); \(\lambda_{ce}\) is the heat conductivity of the cement ring, W/(m\(\cdot\)K); \(\lambda_{ca}\) is the heat conductivity of casing, W/(m\(\cdot\)K); \(\lambda_{t}\) is the heat conductivity of tubing, W/(m\(\cdot\)K); \(d_{b}\) is the diameter of the barefoot hole, m; \(d_{ci}\) is the inner diameter of casing, m; \(d_{co}\) is the outer diameter of casing, m; \(d_{ti}\) is the inner diameter of tubing, m; \(d_{to}\) is outer diameter of tubing, m.  
The heat convection coefficient can be obtained by calculating the fluid Nusselt number. The expressions of \(Nu\) at the inner wall of tubing, Equation (A4), outer wall of tubing, Equation (A5), and outer wall of annulus, Equation (A6), are as follows.  
\[Nu=0.023\text{Re}^{0.8}\text{Pr}^{n} \tag{4}\]\[Nu_{De}=0.02\text{Re}^{0.8}\text{Pr}^{n}(\frac{D_{2}}{D_{1}})^{0.53} \tag{10}\]  
\[Nu_{De}=0.023\text{Re}^{0.8}\text{Pr}^{1/3}(\frac{D_{2}}{D_{1}})^{0.45} \tag{11}\]  
where \(Pr\) is Prandtl number, dimensionless; \(D_{1}\) and \(D_{2}\) are the inner and outer diameters of the annular, m, respectively. All the expressions of \(Nu\) are derived from the Dittus-Boelter formula [34].  
With the sufficient displacement of CO\({}_{2}\) fracturing, the thermal resistance between the stratum and annulus fluid is much smaller than that between the annulus and tubing fluid, according to the calculation, \(R_{at}\) is less than 1% of \(R_{\alpha}\). Therefore, the temperature difference between the fluid in the annulus and tubing can be ignored (i.e., the fluid temperature in the wellbore can be regarded as a whole).
# Two-Stage Robust Lossless DWI Watermarking Based on Transformer Networks in the Wavelet Domain
## 2 Related Works
### Discrete Wavelet Transform  
The 2D discrete wavelet transform [21] (DWT) is a method for decomposing a 2D signal into different frequency components. It can separate an image into low- and high-frequency parts, allowing for image compression, de-noising, and feature extraction. The basic idea is to decompose the signal into multiple scales and different directional wavelet sub-bands, separating the local and global features of the signal for practical analysis and processing.  
The Haar wavelet [22] is the simplest discrete wavelet transform and is the foundation for other wavelet transforms. Compared to other discrete wavelet transforms, the Haar wavelet has lower computational complexity, making it suitable for applications with high computational requirements. The first-level decomposition of the Haar wavelet divides an \(N\times N\) matrix into four sub-matrices of size \(N/2\times N/2\), representing the low-frequency information \(LL_{1}\) that preserves the cover image content, the vertical high-frequency information \(LH_{1}\), horizontal high-frequency information \(HL_{1}\), and diagonal high-frequency information \(HH_{1}\). Based on the first-level decomposition, the low-frequency information \(LL_{1}\) can be used as the input for the next level of decomposition, resulting in four sub-matrices \(LL_{2}\), \(LH_{2}\), \(HL_{2}\), and \(HH_{2}\). The second-level decomposition and reconstruction process of the Haar wavelet for a 2D image is illustrated in Figure 2. For an \(N\times N\)-sized image \(I\), the decomposition formula for the Haar wavelet can be expressed as follows:  
\[\left\{\begin{array}{l}LL(i,j)=(I(i,2j-1)+I(i,2j))/2\\ LH(i,j)=(I(i,2j-1)-I(i,2j))/2\\ HL(i,j)=(LL(2i-1,j)+LL(2i,j))/2\\ HH(i,j)=(LL(2i-1,j)-LL(2i,j))/2\end{array}\right.\left(1\leq i,j\leq N\right) \tag{1}\]
## 3 Materials and Methods
### Data Pre-Processing: Data Normalization  
In the first step, the data should be pre-processed to reduce the implementation time and improve the results. For this purpose, we normalized the data, so that the attributes were normalized as follows. In this work, we performed min-max feature scaling (normalization) for all of the features. It is a scaling methodology where esteems are moved and re-scaled so they wind up somewhere in the range of 0 and 1. The principle for applying normalization is given as follows:  
\[X_{normalized}=\frac{(X-X_{min})}{(X_{max}-X_{min})} \tag{1}\]
## 5 Balancing Experiments
### Balancing Experiments and Results  
The limiting value of the unbalance vibration was set to 0.4 \(\upmu\)m during the balance experiments. When the vibration amplitude exceeded the set value, control commands were sent out and the balance actuator started working. After the vibration amplitude was lower than 0.4 \(\upmu\)m again, the actuator stopped working.  
Active balancing experiments were conducted at three speeds: 9000 rpm, 12,000 rpm, and 15,600 rpm.  
The overall signal and corresponding frequency spectrogram at 9000 rpm are shown in Figure 11. Figure 11 shows that the balance time was within 10 s. Fast Fourier Transformation (FFT) analysis was done, and the frequency spectrogram before and after balancing (Figure 11b) showed that the synchronic vibration was a majority in the overall vibration before balancing, and the balance actuator could efficiently reduce the synchronic vibration to below the limiting value. Through active balancing, the synchronous vibration component decreased from 4.8 \(\upmu\)m to 0.32 \(\upmu\)m, being a 93% decrease. The mass redistribution had little effect on the other harmonic signals, except for the synchronic signal, so the decreased overall vibration was related to the share of the synchronic vibration.  
At the next two speeds, the change in synchronic vibration during the balancing process was as shown in Figure 12. At 12,000 rpm, the initial unbalance vibration was 2.5 \(\upmu\)m and it decreased to 0.23 \(\upmu\)m through active balancing. The amount decreased by 90.8% and the balance time was no more than 10 s. At 15,600 rpm, the initial unbalance vibration was 1.4 \(\upmu\)m and decreased to 0.2 \(\upmu\)m through active balancing. The amount decreased by 85.7% and the balance time was also within 10 s.  
Figure 11: Balancing results at 9000 rpm: (**a**) overall vibration amplitude; and (**b**) FFT analysis.  
The experimental results indicate that the novel active balancing system could effectively decrease the unbalance vibration at three speeds. The amount decreased by more than 80% and the balance time was no more than 10 s.
# A Methodology of Interactive Motion Facades Design through Parametric Strategies
## 1 Introduction  
Facades are an important part of the building that sets the tone for the rest of the building. Facade translates the continuous interaction between the functional activities of the indoor and outdoor architecture. Interactive facades dynamically enhance the translation of activities of the outdoor and indoor space for sustainability, daylighting, aesthetics, information, and data relay through the medium of systematic motion, visual effects, or embedded technology.  
The evolution of interactive facade design is diverse in conceptualization and implementation.  
Interactive facades, such as kinetic facades, have evolved over the years in terms of adaption to the environment, the comfort of building occupants, and the level of technological integration in the facade. [1] Interactive facades that adapt to the environment are configured to optimize the conditions of the building concerning a constantly changing environment. In comparison, static facades' designs are intended to achieve optimized results that are based on fixed prerequisites.  
The decision-making process of interactive facade design involves the consideration of dynamic factors that are involved in the process, such as design generation, materialization, technology, rationalism, and mechanism. The factors of interactive facade design are required to be collectively considered as the successful execution of the design intent is contingent on designing the facade to satisfy all criteria from conceptualization to installation [2].  
The design process of interactive facades combines three-dimensional modeling digital tools with physical models for examination and experimentation. Concurrent iterations to both physical and digital models are required to achieve optimized results, as errors at any stage of the designare potentially exponential in the later stages of construction and maintenance. The complexities of modeling the entire interactive facade through physical modeling are robust and arduous, depending on the scale and type of design, which leads to practitioners modeling a unit of the entire facade as a reference for the digital model iteration.  
The goal of this research is to analyze the existing process of interactive facade design and propose an improved process that combines parametric design strategies and virtual reality to improve the convenience in the workflow of interactive facade design, establish a holistic virtual analysis of the interactive facade, especially the mechanics based on the environment, and generate performance results that can be verified and optimized.
# SERPINB3, Adult-Onset Immunodeficiency, and Generalized Pustular Sporiasis
## 4 Discussion
### SERPINB3 Mutations, Pso p27, and Pustular Skin Reaction  
_SERPINB3_ and _SERPINB4_ are known to be overexpressed in epidermal cells of psoriatic skin [11]. The serpin-derived protein Pso p27, an autoanti in psoriasis and other chronic inflammatory diseases, is proteolytically derived from SERPINB3 and SERPINB4 through non-canonical cleavage in mast cells by chymase [13]. In psoriasis, Pso p27 is a major locally generated immunogen, which is exclusively expressed in psoriatic skin lesions but not in the normal skin [13; 57]. The immune complex formed by Pso p27 and corresponding antibodies activates the complement system and aggravates the inflammatory process. Pso p27 plays roles in the autoimmune reaction in psoriasis [13; 58] and, possibly, in AOID with pustular skin reaction as well. The amount of Pso p27 is correlated to disease activity. Therefore, mutations in _SERPINB3_ in our patients might result in an overexpression not only of SERPINB3 (Figure 4) but also of Pso p27, and this could profoundly aggravate skin inflammation with neutrophil infiltration.  
The genetic variants in other immunodeficiency genes, including _SERPINA5, SERPINA9, IL1 RN_, and _IL1 RL2_, might aggravate the inflammatory process in patients 2 and 3 (Table 1). In this regard, a shared variant in _SERPINA9_ (_Serpin peptidase inhibitor_, _cladeA_, _member 9_; MIM 615677) was identified in both AOID patients (patients 2 and 3). The variant in SERPINA9, an inhibitor of trypsin and trypsin-like serine proteases, might contribute to the pathogenesis of AOID because _SERPINA9_ has important roles in the development and function of B cells [59]. Therefore, alteration of _SERPINA9_ might contribute to the altered autoimmune responses in patients along with the variants in _SERPINB3_. Patient 1 with GPP and patient 2 with AOID carrying the same _SERPINB3_ variant (p.Lys146 Asn) suggests shared pathogenetic mechanisms of GPP and AOID. Our paper will encourage dermatologists and geneticists to screen other SERPIN genes for \"predisposing risk factors\"for GPP and AOID in patients who do not carry mutations in the other known genes for GPP and AOID.
# MODIS Cloud Detection Evaluation Using CALIOP over Polluted Eastern China
## 4 Conclusions  
In this study, we investigated how cloud detection from MODIS cloud mask products performs over Eastern China during 2013-2016 in comparison with observations from CALIOP. Our analysis illustrated that, on average, MODIS and CALIOP were in agreement 82% of the time. Most of the discrepancies were associated with low MODIS CTH (<1.5 km) and low CALIOP COT (<0.4). MODIS observations of clouds undetected by CALIOP were due to a high aerosol optical depth and high solar zenith angle. The ratio of MODIS cloud pixel numbers to CALIOP cloud-free pixel numbers increased largely with an increase in AOD and SZA (2-4 times for AOD and 4-21 times for SZA over different surfaces), indicating that high aerosol load and high SZA may contaminate MODIS cloud detection. CALIOP observed more clouds with a COT less than 0.4 than MODIS, and about 62% of the clouds observed by CALIOP and undetected by MODIS had a COT less than 0.4. That suggested that CALIOP was better at detecting optically thin clouds than MODIS.  
In addition, the consistent proportion observed by both MODIS and CALIOP was the largest for ice clouds. The cloud detection difference between MODIS and CALIOP was mainly ascribed to water clouds, and undetermined-phase clouds contributed the second-most to this difference. Low clouds (Cu and Sc) contributed most of the discrepancies for all surfaces, which is consistent with most of the discrepancies being associated with a low MODIS CTH.  
[PERSON] and [PERSON] conceived and designed the study; [PERSON] performed the calculations and analyses; [PERSON] wrote the paper; [PERSON] and [PERSON] prepared the figures. This research was funded by the National Natural Science Foundation of China (grant No. 41590874) and International Partnership Program of Chinese Academy of Sciences (grant No. 134111 KYSB20180021). The authors thank China National Environmental Monitoring Center for providing air quality data, Level 1 and Atmosphere Archive and Distribution System (LAADS) of the NASA Goddard Space Flight Center for providing MODIS data, and NASA Langley Research Center Atmospheric Science Data Center for providing CALIPSO data. The authors declare no conflict of interest.
###### Abstract  
To maximize the comprehensive utilization of gangue waste, broken gangue can be used to replace gravel as the coarse aggregate to prepare underground roadway shotcrete, and treated gangue powder can be used for the partial replacement of cement. This not only diminishes the demand for conventional raw materials but also increases the amount of gangue waste disposed. Broken gangue waste was ball-milled for 1 h, 3 h, and 5 h to prepare gangue powder, which was used to partially replace cement. Then, experimental schemes for the performance of shotcrete at the rates of cement replacement of 30%, 40%, and 50% were devised to compare the mineral compositions and microscopic characteristics of shotcrete with the partial replacement of cement with gangue powder. The influences of the partial replacement of cement with gangue powder on the slump, tensile strength, and compressive strength of the shotcrete were revealed. The experimental results revealed an inverse relationship between shotcrete slump and both the cement replacement ratio and the gangue Ball-milling duration. Increasing the cement replacement ratio from 30% to 50% reduced slump by 55.3% (103 mm \(\rightarrow\) 46 mm), while extending the Ball-milling time from 1 h to 5 h decreased it by 33.0% (103 mm \(\rightarrow\) 69 mm). Mechanical properties showed contrasting trends: After a 28-day curing process, compressive and tensile strengths declined by 54.5% (20.18 \(\rightarrow\) 9.18 MPa) and 40.4% (1.56 \(\rightarrow\) 0.93 MPa), respectively, with a higher cement replacement ratio. Conversely, prolonged Ball-milling duration improved the compressive strength by 12.8% (18.68 \(\rightarrow\) 21.07 MPa) and the tensile strength by 34.1% (1.26 \(\rightarrow\) 1.69 MPa). Moreover, the shotcrete meets the strength requirements for engineering applications only when the cement replacement ratio is 30% with gangue ball-milling durations of 3 h and 5 h. The research provides strong support for the performance optimization of gangue-based shotcrete and the improvement of the utilization of gangue waste.  
coal-based solid waste; roadway shotcrete; gangue powder; mechanical performance; cement replacement; microscopic characteristics 2025153180
# Recovery of Polyphenols from Agri-Food By-Products: The Olive Oil and Winery Industries Cases
## 4 Polyphenol Analysis: From Quantification to Antioxidant Capacity
### Spectrophotometric Methods
#### 4.2.1 Folin-Ciocalteu Method  
The FC method is a SET assay based on the reaction of phenolic compounds with the FC reagent in alkaline conditions. This reagent is a mixture of phosphomolybdate and phosphotungstate, yellow in color, which, when reduced by the phenolic groups, gives rise to an intense blue complex, with a broad absorption band in the 600-800 nm range. Diverse wavelengths in this range have been reported for the spectrophotometric analysis. The measured absorbance is proportional to the polyphenol content in the sample, which is usually quantified based on a calibration curve of gallic acid, and the results are expressed in terms of gallic acid equivalent (GAE) concentration. The assay is not specific for polyphenols, and other reducing compounds (e.g., carotenoids, vitamin E, vitamin C, reducing sugars, certain amino acids) interfere [13; 67]. However, it is assumed that the reaction of the FC assay with agri-food products is mostly due to polyphenols, and therefore the FC index is a good estimation of polyphenols content for such samples, as evidenced by the correlations reported for FC results and those obtained by other techniques such as LC-UV [66; 68].  
There are different studies on the analysis of TPC by the FC method in residues of olive oil production. For instance, [PERSON] et al. [69] determined the TPC in olive mill wastewater, obtaining values of 2.1-6.43 g GAE L\({}^{-1}\). For olive pomace, [PERSON] et al. [45] and [PERSON] et al. [47] reported TPC values of 0.06-4.37 mg GAE g\({}^{-1}\) dry weight (dw) and 69.66 mg GAE g\({}^{-1}\) dw, respectively. There are also many studies for TPC determination in residues of wine production by the FC method, which is dependent on the extraction procedure used. As a matter of example, some values reported for TPC are 3.22 mg GAE g\({}^{-1}\) dw in grape skin [55], 42.10-188.21 mg GAE g\({}^{-1}\) wet weight (ww) [70], 50.96-219.23 mg GAE kg\({}^{-1}\) dw [57], and 196.2 mg GAE g\({}^{-1}\) dw [58] in grape pomaces, 63.46 mg GAE g\({}^{-1}\) ww in grape steams [70], 188.21 mg GAE g\({}^{-1}\) ww in grape seeds [70], and 26.1 g GAE g\({}^{-1}\) dw [71], 9.84-23.17 mg GAE g\({}^{-1}\) dw [60] in wine lees.
# Implementation of the HHL Algorithm for Solving the Poisson Equation on Quantum Simulators
###### Abstract  
The Poisson equation is a fundamental equation of mathematical physics that describes the potential distribution in static fields. Solving the Poisson equation on a grid is computationally intensive and can be challenging for large grids. In recent years, quantum computing has emerged as a potential approach to solving the Poisson equation more efficiently. This article uses quantum algorithms, particularly the Harrow-Hassidim-Lloyd (HHL) algorithm, to solve the 2D Poisson equation. This algorithm can solve systems of equations faster than classical algorithms when the matrix \(A\) is sparse. The main idea is to use a quantum algorithm to transform the state vector encoding the solution of a system of equations into a superposition of states corresponding to the significant components of this solution. This superposition is measured to obtain the solution of the system of equations. The article also presents the materials and methods used to solve the Poisson equation using the HHL algorithm and provides a quantum circuit diagram. The results demonstrate the low error rate of the quantum algorithm when solving the Poisson equation.  
Poisson equation; quantum computing; HHL algorithm; quantum algorithms; QFT +
Footnote †: journal: Applied sciences