esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis of the Numerical Simulation Results  
### Development Characteristics of Freezing Curtain  
In order to study the development law of the frozen wall at different times, the volume of the model temperature at \(-1\)\({}^{\circ}\)C and below is extracted, and the development of the frozen wall at different freezing times is obtained as shown in Figure 4.  
From the development diagram of the frozen wall, it can be intuitively obtained that the soil and the freezing pipe undergo intense heat exchange, and the frozen soil in the front space of the shield cutter head expands rapidly outward from each freezing pipe when the freezing process is 30 days. The frozen soil between the four freezing pipes at the top, the four freezing pipes at the bottom, and the eight freezing pipes in the middle develops rapidly and gradually connects into a whole. However, the gap is still unfrozen soil because of the long distance between the middle and the freezing pipes at the top and the bottom. The range of frozen soil in the middle and the top and bottom gradually expands and connects to form a complete cup bottom-shaped frozen wall when it is frozen for 40 days. However, there are four opening areas in the cup wall due to the influence of the opening and arrangement spacing of the freezing pipe; in other words, there are weak areas of frozen soil at the four corners of the freezing plate. The opening direction of frozen soil is consistent with the divergence direction of the maximum spacing freezing pipe in the outer ring. With the development of freezing time, the bottom of the cup becomes  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Material**} & \multirow{2}{*}{**Density (kg/m\({}^{3}\))**} & \multicolumn{2}{c}{**Thermal Conductivity (W/m/K)**} & \multicolumn{2}{c}{**Specific Heat Capacity (J/Kg/K)**} \\ \cline{3-6}  & & **Frozen** & **Unfrozen** & **Frozen** & **Unfrozen** \\ \hline Shield machine & 7800 & / & 36 & / & 460 \\ Sandy soil layer & 1800 & 1.7 & 1.4 & 1340 & 1690 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Thermophysical parameters of engineering.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Time/d** & **Temperature/\({}^{\circ}\)C** & **Time/d** & **Temperature/\({}^{\circ}\)C** \\ \hline
1 & 24.6 & 19 & \(-21.6\) \\
2 & 16.9 & 21 & \(-26.1\) \\
5 & 2.2 & 22 & \(-27.8\) \\
7 & \(-7.8\) & 23 & \(-28.0\) \\
8 & \(-10.8\) & 24 & \(-28.1\) \\
9 & \(-14.4\) & 25 & \(-27.1\) \\
10 & \(-21.1\) & 27 & \(-28.4\) \\
11 & \(-22.9\) & 30 & \(-29.0\) \\
13 & \(-24.8\) & 60 & \(-30.0\) \\
14 & \(-25.3\) & 90 & \(-30.0\) \\
15 & \(-25.1\) & 120 & \(-30.0\) \\
16 & \(-25.4\) & 150 & \(-30.0\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: The cooling situation of brine.  
thicker and flatter gradually, the opening area of the cup wall becomes smaller gradually, and finally forms a freezing curtain that meets the requirements of design.
## 3 Federated Kalman Filter Design
### Inertial/Odometer Dead-Reckoning Error Model
#### 3.2.1 Position Error Equation of Dead Reckoning  
The measurement error of the odometer data was mainly due to the calibration coefficient error \(\delta\text{K}_{\text{D}}\). Thus, the odometer speed error equation can be obtained as follows:  
\[\delta\text{V}_{\text{D}}^{\text{n}}=\text{V}_{\text{D}}^{\text{n}}\times \varphi_{\text{D}}+\delta\text{K}_{\text{D}}\text{V}_{\text{D}}^{\text{n}} \tag{6}\]  
where \(\text{V}_{\text{D}}\) is the actual running speed of the odometer and \(\varphi_{\text{D}}\) is the attitude angle error. The position error equation of dead reckoning is shown in Equation (7):  
\[\left\{\begin{array}{c}\delta\dot{\text{L}}=\frac{\text{s}\text{V}_{\text{N }}}{\text{R}_{\text{M}}+\text{H}}\delta\text{K}_{\text{D}}-\frac{\text{V}_{ \text{N}}}{\text{R}_{\text{M}}+\text{H}}\varphi_{\text{U}}\\ \delta\dot{\lambda}=\frac{\text{s}\text{c}\text{K}_{\text{N}}+\text{H}}\delta \text{V}_{\text{E}}+\frac{\text{V}\text{s}\text{c}\text{L}\text{a}\text{L} }{\text{R}_{\text{N}}+\text{H}}\delta\text{L}-\frac{\text{V}\text{s}\text{ s}\text{c}\text{L}}{\text{R}_{\text{N}}+\text{H}}\delta\text{H}\\ \delta\dot{\text{H}}=\delta\text{V}_{\text{U}}\end{array}\right. \tag{7}\]  
where \(\delta\text{V}_{\text{E}}\), \(\delta\text{V}_{\text{N}}\), and \(\delta\text{V}_{\text{U}}\) are the components of the odometer speed error along the XYZ axis in the navigation coordinate system. Equation (7) was expanded to obtain Equation (8):  
\[\left\{\begin{array}{c}\delta\dot{\text{L}}=\frac{\text{V}_{\text{N}}}{ \text{R}_{\text{M}}+\text{H}}\delta\text{K}_{\text{D}}-\frac{\text{V}_{\text {E}}}{\text{R}_{\text{M}}+\text{H}}\varphi_{\text{U}}\\ \delta\dot{\lambda}=\frac{\text{V}_{\text{N}}\text{s}\text{s}\text{c}\text{L} }{\text{R}_{\text{N}}+\text{H}}\varphi_{\text{U}}+\frac{\text{V}\text{s} \text{s}\text{c}\text{L}\text{a}\text{L}}{\text{R}_{\text{N}}+\text{H}} \delta\text{L}-\frac{\text{V}\text{s}\text{s}\text{c}\text{L}}{\text{R}_{ \text{N}}+\text{H}}\delta\text{K}_{\text{D}}\\ \delta\dot{\text{H}}=\delta\text{K}_{\text{D}}\text{V}_{\text{U}}\end{array}\right. \tag{8}\]
## 3 Results
### Localization of MSPL/TMPRSS13 in Colorectal Carcinoma Tissue  
The expression of MSPL/TMPRSS13 has been reported in normal tissues such as lung, skin, and prostate, but not in the colon [19; 23]. To assess its expression in human colorectal cancerous and adjacent noncancerous tissues, we performed fluorescent immunohistochemistry (Figure 2). We found that MSPL/TMPRSS13 is highly expressed in various colorectal cancerous tissues but has lower expression in noncancerous tissues. Notably, we observed intense staining of MSPL/TMPRSS13 at the apical epithelial surface of various colorectal cancer tissues, whereas weak, broad staining was observed throughout the noncancerous mucosa. These results suggest that, during oncogenesis, localization of MSPL/TMPRSS13 shifts to the luminal side of the colon epithelium, where MSPL/TMPRSS13 obtains its DC-SIGN-bindable glycan structure.  
The expression of MSPL/TMPRSS13 (green) and DC-SIGN ligands (red) was visualized through laser confocal microscopy. Yellow fluorescence indicates merged green and red signals, as shown in the right panels. Nomarski images are shown on the right side of each panel. Scale bars, 100 \(\upmu\)m.  
Figure 1: Identification of mosaic serine protease large-form (MSPL)/transmembrane protease serine 13 (TMPRSS13) as novel ligands of dendritic cell-specific intercellular adhesion molecule-3-grabbing nonintegrin (DC-SIGN) in colon cancer COLO205 cells. (**a**) DC-SIGN ligands were purified through affinity chromatography. The 90-kDa and 100-kDa protein bands were both excised and analyzed by liquid chromatography–mass spectrometry (LC-MS/MS). The arrowheads indicate proteins eluted from the affinity column. (**b**) Identification of TMPRSS13 through LC-MS. The detected peptide sequences are underlined with red (upper band) and green (lower band) lines. (**c**) Domain structures and potential N-glycosylation sites of MSPL and TMPRSS13. TR, tandem repeat domain; TM, transmembrane domain; SRCR, scavenger receptor cysteine-rich domain; SPD, serine protease domain. (**d,e**) Glycan-dependent interaction of DC-SIGN with recombinant MSPL and TMPRSS13 expressed in HEK293 cells. (**d**) HEK293 cells were transfected with full-length MSPL or TMPRSS13. The cell lysates were subjected to western blotting (WB) using anti-FLAG M2 antibody. (**e**) Lysates of MSPL and TMPRSS13 transfectant cells were incubated with DC-SIGN-Fc beads. Pulled-down proteins were subjected to Western blotting (WB) using anti-FLAG M2 antibody. (**f**) Staining of HEK293 MSPL transfectant cells with anti-TMPRSS13 antibody. (**g**) Double staining of COLO205 cells with DC-SIGN-Fc and anti-TMPRSS13 antibody.
# On the Use of LoRa Technology for Logic Selectivity in MV Distribution Networks
## 3 LoRa Technology
### LoRa Packet Structure  
A LoRa packet consists of three elements: a preamble, an optional header, and a payload [20]. The preamble is used to allow the receiver to identify the beginning of a packet and synchronize with the transmitted data. The header provides information on the payload: its length expressed in bytes, the forward error correction code rate, and the presence of an additional 16 optional CRC (cyclic redundancy check) bits. It is worth noting that the header is optional and, in some scenarios where payload, coding rate, and CRC configuration are known, it is advantageous to eliminate it in order to reduce the transmission times.  
For a given combination of SF, coding rate CR and signal bandwidth BW, the total transmission time of the LoRa packet, called \(T_{packet}\), is given by:  
\[T_{packet}=T_{preamble}+T_{payload}=\left[\left(n_{preamble}+4.25\right)+n_{ payload}\right]\cdot T_{symbol} \tag{10}\]  
where \(n_{preamble}\) is the programmable length of the preamble and \(n_{payload}\) is given by the following formula:  
\[n_{payload}=8+max\left(ceil\left[\frac{\left(8\text{PL}-4\text{SF}+28+16\text {CRC}-20\text{IH}\right)}{4\left(\text{SF}-2\text{DE}\right)}\right]\cdot \left(\text{CR}+4\right),0\right) \tag{11}\]  
where (_i_) PL is the length, in bytes, of the payload, (_ii_) IH is equal to 1 if the header is implicit, 0 otherwise, (_iii_) DE is set to 1 if the low data rate optimization is used, 0 otherwise, and (_iv_) CRC is 1 if the CRC for the payload is enabled, 0 otherwise.  
As an example, in Table 5\(T_{packet}\) values are shown based on values of BW and SF, with a preamble of 8.25 bytes, explicit header, CR equal to 1, a payload of 10 bytes, and enabled CRC.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**BW (kHz)** & **SF** & **Sensitivity (dBm)** \\ \hline
125 & 8 & \(-126\) \\
125 & 10 & \(-132\) \\
125 & 12 & \(-137\) \\ \hline
250 & 8 & \(-123\) \\
250 & 10 & \(-129\) \\
250 & 12 & \(-134\) \\ \hline
500 & 8 & \(-120\) \\
500 & 10 & \(-126\) \\
500 & 12 & \(-131\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Sensitivity values depending on BW and SF, with CR = 1.  
As it can be easily noted, the transmission time can vary considerably from a configuration with SF = 7 and BW = 500 kHz (\(T_{packet}\) = 9.28 ms) to a configuration with SF = 12 and BW = 125 kHz, characterized by transmission times of almost 1 s (\(T_{packet}\) = 860.16 ms).  
It is evident that this feature, which also outlines the limitations of LoRa technology [21], has a crucial impact on the design of a logic selectivity system.
# PFSegIris: Precise and Fast Segmentation Algorithm for Multi-Source Heterogeneous Iris
## 2 Methods
### Encoder  
The function of an encoder is to extract information about the location, shape, and size of iris regions in multi-source heterogeneous iris images. Only when the encoder is able to extract accurate and detailed iris features will the iris segmentation results be good. Therefore, designing a suitable encoder for iris segmentation is of great importance to the network model.  
The encoder designed in this paper included four blocks, and each block included two different iris feature extraction modules: iris-feature module1 and iris-feature module2, which are shown in Figure 2a,b, respectively. The iris-feature modules include four layers: a mixed depthwise convolution, a pointwise convolution, a pointwise convolution, and a depthwise convolution, in which no activation function was added after the second and the fourth layers.  
Depthwise convolutions with kernels of different sizes encode the high-dimensional features of the iris image, generating rich representations, which is superior to the traditional depthwise convolution suffering from the limitations of a single kernel size [28]. The 3 \(\times\) 3 convolution kernels have a strong ability to perceive image details and can fully perceive the iris in a small area, while the 5 \(\times\) 5 convolution kernels have a larger receptive field and can capture iris information in a larger area. Two pointwise convolutions were used to encode information between channels, with channel reduction followed by expansion. The depthwise convolution at the end can learn more abundant iris spatial information and, thereby, exhibits better segmentation performance. In order to obtain more information from the bottom layers and promote the propagation of gradient information cross layers, shortcuts were used to connect high-dimensional representations with a large number of channels and help the network avoid degradation as the depth increases. Table 1 shows  
Figure 1: Overview of PFSegIris.  
the structure details of the iris-feature module. The structure of the encoder was able to remarkably reduce the number of parameters and calculations of the model while taking the segmentation accuracy into account, which greatly reduced the computation cost.
## 3 Parameter Determination Method for the JH-2 Model
### Determination of Damage  
Damage (D) describes the transition from intact to fractured strength [26] and begins to accumulate when brittle material starts to flow plastically and then is softened due to a change from large to smaller particle size. A value of \(D=1\) is defined when the material is completely damaged. According to the definition of constants \(D1\) and \(D2\) in Equation (7), if the tendency change of the pressure dependent fracture strain \(\varepsilon_{f}^{p}\) following the pressure \(P^{*}\) can be determined by tests, it would be easy to achieve the constants \(D1\) and \(D2\) by fitting. [PERSON] et al. [37] performed both tension and compression tests on Barre granite during quasi-static procedures as well as split Hopkinson bar techniques, in which the rock was subjected to a straining deformation at a constant strain rate. The increase of fracture strain presents nonlinear behavior following the increase of constant pressure, as shown in Figure 7 from [PERSON]'s data. Fitting the data based on Equation (7) gives the constants \(D1=0.008\) and \(D2=0.435\). If there are not enough data, or it is difficult to measure plastic strain directly, especially for some low ductility materials, in order to depict the relationship between fracture strain and pressure, numerical adjustment is another valid method to obtain the two constants \(D1\) and \(D2\), to which the computational results can be compared with ballistic experiments that produce dwell, interface defeat and penetration. The detailed method of numerical adjustment is described in [26; 29; 47]. According to the empirical data, the suggested range of values are \(D1\in[0.001,0.009]\) and \(D2\in[0.4,2.0]\).  
All the obtained parameters of Barre granite are listed in Table 1. In order to verify the validity of the parameters computed and extrapolated above for the JH-2 constitutive model, some comparison works were performed as follows. These obtained parameters are adopted in 3D numerical simulations using arbitrary Lagrangian-Eulerian (ALE)/JH-2 method based on the prototype laboratory rock blasting experiments. Both the crack patterns and measured pressures are in agreement with the results from the lab-scale experiments. The attenuation curves of the pressure and particle peak velocity (PPV) along the radial direction were determined, and the summarized theoretical formulae are deemed to be reasonable through comparison with previous theoretical formulae. Additionally, comparisons of blasting tests separately carried out by the DEM-SPH hybrid method [52] and ALE/JH-2 method demonstrate similar crack patterns formed both in intact rock disks and jointed rock disks. This demonstrates that the computational and extrapolated parameters are reasonable for the JH-2 constitutive model of rock materials. A more detailed description of the process and results of the numerical simulations mentioned above has been elaborated elsewhere [53].  
Figure 6: Normalized equivalent stress against normalized pressure for intact and fractured granite.
## 1 Introduction
Several major contributions have been presented in this paper and are summarized as follows:  
1. The communication aspects of VLP systems that use QADA are characterized for the first time. To do this we first calculate the channel gain as a function of the receiver properties. We then analyze the signal-to-noise ratio (SNR) performance of each quadrant individually. The performance is investigated at different locations and for different orientations of the receiver. The results show that reliable communication can be achieved in VLP systems that use the QADA receiver. 2. The diversity among the four quadrants of the receiver is described for the first time, and it is shown how this diversity can be exploited to improve performance. This is done by considering three different techniques. In the first, the signal from the quadrant with the maximum channel gain is selected. In the second and the third, equal gain combining (EGC) and maximum ratio combining (MRC) are applied to the signals of the four quadrants. 3. The relation between the aperture height in a QADA and its FOV is analyzed. In this new analysis, two QADAs with two aperture heights are considered. The communication FOV and positioning FOV are clearly described and are shown to be different. 4. The communication performance of a VLP system using a QADA is investigated in a typical room equipped with four luminaires. 5. Experimental results confirm the very reliable communication performance of the QADA and demonstrate the importance of diversity combining. The result is a comprehensive analysis of the communications properties of a VLP system using a receiver specifically designed for positioning.
# Laminar _Ulva_ Species: A Multi-Tool for Humankind?
## 5 Commercial Potential
### Biofuel  
Fossil energy has been a key driver of the technical, social, economic, and developmental progress that has trailed since the Industrial Revolution between 1760 and 1820/1840. Fossil fuels (coal, oil, gas) continue to play a dominant role in global energy systems. It is estimated that around 136,018 TWh were consumed in 2021 [209]. However, when burned, they emit carbon dioxide (CO\({}_{2}\)), which is the leading cause of global climate change and air pollution. One option is the usage of biofuels.  
Biofuels are a form of energy derived from biomass. Currently, bioethanol from sugar cane or maize, as well as biodiesel from oilseed plants, are generated on a wide scale thanks to environmentally friendly oil extraction and well-established farming processes [210]. However, recent developments in the generation of biofuels from food crops have highlighted numerous ethical problems due to the food vs. fuel debate, permitting openness and interest in algae.  
According to [PERSON] et al., 2020 [210], the production of biodiesel and bioethanol from 22 most promising seaweed species, harvested from Abu Qir Bay, Egypt, was assessed using three distinct methods. The first approach included producing biodiesel by extracting lipids and then trans-esterriving them. The second technique included directly fermenting all biomasses to make bioethanol without first extracting the lipids. The third approach involved producing biodiesel from the extracted lipids, followed by fermenting the remaining lipid-free biomass to make bioethanol. They found that _Ulva intestinalis_ presented the highest value of relative increase in energy compounds; the third route increased the efficiency of hydrolysis and the availability of sugar, which resulted in a 14.1% higher bioethanol yield; and the sequential recovery of energy presented gross energy production 170.9% and 82.0% higher than the individual production of biodiesel or bioethanol, respectively. [PERSON] et al., 2015 [211] developed a simultaneous saccharification and fermentation process based on sonication to produce bioethanol from _Ulva rigida_ and generated a maximum of 6.2 wt% ethanol pursuant to sonication in 3 h (65.5%) and only 4.9% by weight of ethanol under incubation even after 48 h (67.9%).  
However, according to the research, bioethanol produced from sugar cane or other agricultural crops is significantly less expensive than the _Ulva_ biorefinery, independent of the technique. As a result, efforts to develop a competitive supply of bioethanol from green algae will not be financially feasible in the future.
# Processing of EMG Signals with High Impact of Power Line and Cardiac Interferences
## 4 Conclusions  
In this research, the advanced three-stage signal processing of EMG recordings was presented. The IIR filtering was implemented to remove low-frequency drift, 50/60 Hz power line, and ECG heart beating interferences to achieve satisfactory diagnostic data. The RMS signal varying in time was calculated for the user-defined window moving along  
Figure 16: Example of analysis, (a) a raw superimposed EMG measured on the left pectoral muscle with very high ECG signal contribution, (b) RMS-80/40 EMG filtered by HP Butterworth filter (\(f_{c}\) = 200 Hz).  
Figure 17: RMS-EMG signal of the right abdominal muscle with high content of power line parasitic disturbances, HP Butterworth filter (\(f_{c}\) = 20 Hz), (a) 40/40 (length/offset), (b) 80/80.  
the recorded samplings. The proposed algorithm consisting of the different banks of filters was successfully implemented in the portable DSP system.  
The proposed algorithm is a compromise between the quality of processing and the complexity of calculus. Removing the superimposed parasitic signals by IIR filtering leads not only to registered data improvement but to EMG signals deteriorating as well. The proposed processing of EMG signals enables cleaning up of the EMG signal with minimal loss of the diagnostic content. The algorithm is fully implemented in software using pipeline processing. One must bear in mind that the overall processing has to run in real-time on a low-power and cost-effective DSP microprocessor system. In the authors' opinion, the EMG systems have to be parameterized to choose the signal processing appropriate for a patient and his diagnosis.  
investigation, K.S. and M.K.; project administration, V.M.; validation, D.S., resources, P.C., G.L. and K.T.; writing--original draft, B.W. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Intelligent Development Operational Program 2014-2020 co-financed by the European Regional Development Fund, project POIR.04.01.04-00-0074/19.  
Not applicable.  
The authors declare no conflict of interest.