esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
1. The formation of the frozen curtain during the freezing process is mostly dependent on two types of freezing tubes to freeze the soil between the jacked pipes and fulfill the objective of sealing water. At 90 days, the thickness of the frozen soil curtain varies from 2.32 m to 2.58 m, and the lower and more homogeneous frozen soil temperature between adjacent pipes ensures that its strength fulfills the water-sealing safety requirements;
2. The frost heave displacement distribution and fluctuation throughout the freezing phase are highly connected to geological conditions, freezing scheme, frozen soil curtain development, and pipe curtain structure. The ground surface's vertical frost heave displacement curve is normally distributed with the tunnel centerline (x = 0 m) as the symmetry axis, and the change is more noticeable in the range of \(-30\) m \(\leq\) x \(\leq\) 30 m; the maximum value at any time is located at the centerline, which is 155.67 mm at 90 d. The horizontal displacement at the ground surface's center line is always zero, while the values on both sides of the centerline progressively rise with distance, reaching an extreme value at x = 15.74 m, 59.63 mm at 90 d;
3. In this study, the geological conditions, temperature gradient, and numerical model are appropriately simplified, and the calculation object is the two-dimensional cross-section of the tunnel during the freezing phase. Although the calculation results are in good agreement with the measured data and have good guiding value for engineering construction, it is still a topic worth further research on how to more accurately reflect complex actual working conditions and focus on establishing more precise large-scale numerical models considering subsequent construction phases, such as tunnel excavation, support structure construction, and ground thawing settlement.  
Figure 11: Comparison of simulated and measured vertical displacement of ground surface midpoint.  
**Author Contributions:** Conceptualization, Y.D.; methodology, Y.D.; software, Y.D. and W.L.; validation, Y.D. and C.R.; writing--original draft preparation, Y.D.; writing--review and editing, Y.D. and W.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Scientific Research Foundation for High-level Talents of Anhui University of Science and Technology, grant number 2022 yirc55; and the National Natural Science Foundation of China, grant number 51878005.  
**Data Availability Statement:** The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Maintenance Schemes
### Hammer Peeting Strengthening
To validate the efficacy of the implemented preventive measures and quantitatively assess the structural modifications induced by the hammer peening, comprehensive in situ stress monitoring was conducted on an operational bridge, with the experimental setup illustrated in Figure 13. Stress response data were acquired under actual traffic loading conditions at a sampling frequency of 256 Hz, enabling a detailed comparison of the stress histories at DU weld locations before and after the treatment, as presented in Figure 14. The pre-treatment measurements revealed that the DU welds exhibited characteristic tension-compression stress cycles or pure tensile stress cycles. Following hammer peening implementation, compressive residual stresses averaging 250 MPa were successfully introduced, resulting in the complete transformation of stress cycle patterns to predominantly compressive states. The measured residual compressive stresses were oriented normal to the weld direction. Figure 12: Process of hammer peening. Figure 13: Hammer peening operation on the deformation area in a real bridge. The magnitude of the Induced compressive stresses was demonstrated to significantly exceed that of the deformation-induced tensile stress components, effectively establishing a compressive stress regime at the treated DU weld locations under operational loading conditions. This stress state modification, characterized by favorable stress ratio alterations, has been shown to effectively delay fatigue crack initiation and propagation. The experimental results conclusively demonstrate that hammer peening represents a non-invasive strengthening technique capable of substantially enhancing the fatigue performance in deformation-affected DU welds while maintaining structural integrity.
# An Improved CASA Model for Estimating Winter Wheat Yield from Remote Sensing Images
## 3 Study Methods
### NPP-Yield Conversion Model  
Winter wheat yield is the grain harvest of winter wheat, which often refers to the economic yield of winter wheat. Along with [PERSON] et al. [65], the method was used to estimate winter wheat yield, which is  
\[\text{Yield}=\frac{\alpha\sum\text{NPP}\times p\times\text{HI}}{1-\omega} \times 10^{-2} \tag{19}\]  
where Yield is the winter wheat yield [t ha\({}^{-1}\)], \(\alpha\) is the carbon-conversion coefficient (the carbon content of winter wheat is about 45%, so the \(\alpha\) = 2.22 [66]), \(\sum\) NPP is the cumulative net primary production of organic matter over the entire growing season of winter wheat, \(p\) = 0.9 is the distribution coefficient of aboveground parts, \(\omega\) = 12.5% is the water-content coefficient of wheat grain during the storage period after harvest [67], and HI = 0.45 is the harvest index (this value is based on the growth characteristics of winter wheat in the Tongzhou and Shunyi districts of Beijing, as determined by previous research), \(10^{-2}\) is the conversion factor between the unit g m\({}^{-2}\) and t ha\({}^{-1}\).
## 4 Conclusions  
It is commonly accepted in the literature that the main cause of voltage asymmetry in electric power networks is the unbalanced loading of individual phases, resulting mainly from the work of single-phase loads or sources. As shown in this paper, the value of the zero-sequence voltage asymmetry factors \(\alpha_{\text{U0}}\) are also influenced by the earth fault compensation detuning factor, i.e., the reactance of the reactor earthing the neutral point of the MV network (Petersen coil). The values of the \(\alpha_{\text{U0}}\) factor can vary in a very wide range from 0.3% to even 90%. No such dependence was observed (except in the case of the resonance condition s = 0%) for the negative-sequence voltage asymmetry factor \(\alpha_{\text{U2}}\), which does not depend on the inductance of the Petersen coil installed in the network. As can be seen from the authors' research and analysis, the values of the zero- and negative-sequence voltage asymmetry factor depend strictly on the distance from the substation and the cross-section of the conductors used for the overhead lines. In both cases, the values of both asymmetry factors decrease as the cross-section increases. Increasing the distance from the substation (independently of the detuning factor) increases the values of the \(\alpha_{\text{U0}}\) and \(\alpha_{\text{U2}}\) factors, but the effect is noticeably smaller after a certain distance (approx. 18-20 km). The relationships presented in this paper can be very important in the design of earth fault protection to avoid tripping faults and in the design of railway networks to reduce current pulsations.  
Conceptualization, G.H. and Z.S.; methodology, G.H.; software, G.H.; validation, G.H. and Z.S.; formal analysis, G.H.; investigation, G.H.; resources, G.H.; data curation, G.H.; writing--original draft preparation, G.H.; writing--review and editing, Z.S.; visualization, G.H.; supervision, G.H.; project administration, Z.S.; funding acquisition, G.H. and Z.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by scientific work WZ/WE-IA/7/2023 conducted in the Department of Electrotechnics, Power Electronics and Electrical Power Engineering at Bialystok University of Technology.  
Not applicable.  
Data available at B.  
Data is contained within the article.  
The authors declare no conflicts of interest.
## 4 Laboratory Tests
### Phase 2: Materials and Physicochemical Characterisation
#### 4.1.3. Nanomaterials  
Three types of silica-based nanomaterials were used in this study, named N1, N2 and N3.  
N1 is a concentrated liquid solution of sodium silicate for soil stabilisation, together with binders such as lime or cement. N2 is an organosilane, and N3 is a silica-based acrylic copolymeric in aqueous solution form, intended to be used together in a 1:1 ratio.  
For the three nanomaterials, its chemical composition was obtained by an X-ray fluorescence (XRF) analysis and a thermogravimetric analysis (TGA). Table 3 shows the results of the XRF analysis, and Figure 5 shows the results of the TGA.  
As can be observed in Table 3, the composition of N1 was mainly silicon and sodium, elements that form the nano-sized sodium silicates that make up this material. N2 and N3 were composed only of silicon as the main element and traces of the other elements. This sodium is dissolved in the form of organosilanes in N2 and in the form of an acrylic copolymer in N3.  
The results of the TGA analysis are shown in Figure 5. In N1, a progressive weight loss was observed of up to 200 \({}^{\circ}\)C, which was due to the loss of water in the nanosilane. This nanomaterial showed a high silica content of 32%. N2 showed a higher weight loss; the silica content at 400 \({}^{\circ}\)C was 17%. N3 completely loses its mass at a temperature of 400 \({}^{\circ}\)C.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Composition (\%)/Binder** & **SiO\({}_{2}\)** & **Al\({}_{2}\)O\({}_{3}\)** & **Fe\({}_{2}\)O\({}_{3}\)** & **CaO** & **MgO** & **SO\({}_{3}\)** & **K\({}_{2}\)O** \\ \hline CEM II/B-L 32.5R & 16.20 & 3.83 & 2.75 & 60.41 & 0.84 & 2.64 & 0.64 \\ CL 90-Q & 2.37 & 1.47 & 0.21 & 57.26 & 13.78 & 0.72 & 0.07 \\ \hline \end{tabular}
\end{table}
Table 2: Compositions of the binders.  
Figure 4: XRD of sandy soil.  
The higher stability of silica in the form of sodium silicate compared to other solutions, such as organosilanes or acrylic copolymers were observed, organosilane compounds lost 83% of their mass and acrylic compounds did not retain their mass after the test.
## 3 Results
### VOC Profile of C. depressa Leaves of Different Origins
Aldehydes such as hexanal, (_E_)-2-hexenal, octanal, and nonanal were located near the center and were considered characteristic of the two Taiwanese accessions and several Okinawan accessions. Differences in the VOC composition of _C.depressa_ according to the cultivation regions were further visualized using OPLS-DA plots (Figure 3e,f). The score plots of the VOC data effectively distinguished all the accessions based on their origins. The plot associated Kagoshima accessions with monoterpenes (pipertone, (_Z_)-ocimee, citronellal, and \(\alpha\)-phellandrene); Okinawa accessions with sesquiterpenes (\(\alpha\)-caryophyllene, \(\beta\)-elemene, and bicyclogermacrene), decanal, and 5,9,9,-trimethyl-spiro[3.5]non-5-en-1-one; and Taiwanese accessions with hexanal (Figure 3f). The most distinctive VOCs were all from 100 detected chromatographic peaks and were calculated and represented in a volcano plot (Figure 4) based on the selection criteria of VIP > 1.0, FDR < 0.05, and log\({}_{2}\)FC > 1.0. As for the distinguishable VOCs between Okinawan and Kagoshima accessions, leaves of Okinawan accessions were significantly higher in terpen, such as \(\alpha\)-cubehene, carvcard, and \(\beta\)-elemene, and a ketone, namely 5,9,9,-trimethyl-spiro[3.5]non-5-en-1-one (log\({}_{2}\)FC = 1.10, 1.07, 1.03, and 1.02, respectively). Conversely, Kagoshima accessions were significantly higher in 10 monoterpenes which include piperitone, thym methyl ether, citronellal, (_Z_)-\(\beta\)-ocimee, and 1,8-cinee (log\({}_{2}\)FC = 7.48, 7.01, 6.57, 4.30, and 3.31, respectively) (Figure 4a, Supplementary Table S3). Moreover, between Okinawan and Taiwanese accessions, three VOCs including decanal, \(\alpha\)-caryophyllene, and 5,9,9,-trimethyl-spiro[3.5]non-5-en-1-one (log\({}_{2}\)FC = 3.87, 3.27, and 1.48, respectively) were significantly higher in Okinawan accessions rather than the ones of Taiwan, with four compounds including hexanal, camphene, terpinene-4-ol, and \(\alpha\)-terpineol (log\({}_{2}\)FC = 2.27, 1.09, 1.06, and 1.03, respectively) which were significantly higher in Taiwan than in Okinawa (Figure 4b, Supplementary Table S3). Furthermore, between Kagoshima and Taiwan accessions, those of Kagoshima were found to be greater in monoterpenes and aldehydes such as piperitone, citronellal, (_Z_)-\(\beta\)-ocimee, decanal, and 1,8-cineeole (log\({}_{2}\)FC = 6.38, 5.17, 4.08, 3.56, and 3.28, respectively), while Taiwanese accessions were significantly higher in aldehydes and sesquiterpenes as follows: hexanal, \(\alpha\)-cadinene, selina-3,7(11)-diene, \(\beta\)-selinenol, \(\alpha\)-maaliene (log\({}_{2}\)FC = 2.19, 1.25, 1.08, 1.08, and 1.06, respectively) (Figure 4c, Supplementary Table S3). Of these three regional comparisons, Kagoshima accessions had significantly greater intensities of piperitone and citronellal, as indicated by their large log\({}_{2}\)FC values compared to Okinawan and Taiwanese accessions.
# Constrained Backtracking Matching Pursuit Algorithm for Image Reconstruction in Compressed Sensing
## 3 The Constrained Backtracking Matching Pursuit Algorithm for Image Reconstruction
Specifically, the estimated sparsity level is far smaller than the true one at the early stage. Based on this theorem, the energy criterion can be improved by introducing a parameter \(a\) to constrain the reconstruction stages. Inspired by the \"four-to-one\" practical rule proposed in [27], the measurement number should be four-times the signal sparsity level for signal reconstruction. In CBMP, \(\text{U}_{i}\) is the union of a new matched set and the estimated support set of the previous iteration. \(M\) is the row number of the measurement matrix. We introduce the \"four-to-one\" rule to CBMP and use the parameter \(b\) to constrain the estimation stage. The relationship between the parameters \(a\) and \(b\) is analyzed in Section 4. Figure 3 shows the flowchart of the CBMP algorithm. The reconstruction process is divided into the sparsity level update stage and the support set update stage. As for the details, the sparsity level update stage includes both the large and small step size update stages. In the early stage of reconstruction, the estimated sparsity level is far less than the true one, so large step sizes are adopted to estimate the sparsity level. As the iteration goes on, after the threshold condition is satisfied, it enters a small step size stage. The reason why CBMP can achieve better reconstruction performance than SAMP is attributed to its superior capability in handling the wrong indices (atoms). When the current obtained sparsity level is far less than the true one, those false indices can be easily added into the candidate support set. However, these false indices are difficult to eliminate in the later iteration. Therefore, at the beginning of the iteration, a large step size allows those false indices to be filtered out.
# Bit Streaming Processing Algorithms for Intelligent Hardware Converters
## 2 Materials and Methods  
From the structural point of view, bit stream processing algorithms correspond to processes with negative feedback, aimed at achieving an equilibrium state characterizing the final result. Step-by-step calculations in this case are absent, and mathematical transformations are obtained in a single process of result formation.  
The conversion of binary code to bit streams is simple. Hence codes can be processed using functional transformation algorithms of bit stream. In this case, the computational processes are based on the following operations:  
* Pulse frequency sweep (PFS);
* Pulse-width sweep (PWS).  
By using PFS we convert the binary codes into PFM bit stream, i.e., bit stream characterized by the number of single pulses per unit time. By means of PWS, we transform the binary codes to the PWM signal, i.e., relative duration of the active signal value per unit of time.
## 5 Computational Experiments
### Computational Experiments for Static Problems  
#### 5.3.1 Experimental Setup  
Our proposed method is compared with the conventional route planning methods in a static problem environment, where all tasks are known. As conventional methods, ADRPM (Autonomous Distributed Route Planning Method) [50] proposed by [PERSON] et al. (2003), and the Q-learning method [49] proposed by [PERSON] et al. (2020) were utilized. A characteristic feature of ADRPM is that the optimal path is derived when there is no collision or interference between AGVs. Even in the case of collision or interference, a near-optimal path can be generated. By using the method based on Q-learning, the number of deadlocks that occur as the number of AGVs increases can be avoided, and the traveling time can be reduced. In addition, conflict-based search (CBS) [14] was used as the conventional method. The CBS classifies problems into two categories: the high level and the low level. At a high level, a conflict tree is created based on collisions between individual agents. At a low level, a search is performed that satisfies the constraints imposed by the high-level tree structure.  
Considering the experiments in a static environment, initial and destination positions are given to each AGV in advance as tasks. The number of AGVs was increased one by one from 1 to 15, and under each condition, the total transfer period taken for all AGVs to complete their tasks and the computation time required for route planning were measured. Here, 10 types of transfer tasks were prepared for each different number of AGVs, and the averages of the 10 repeated experiments for each task were compared. The layout was fixed with a bidirectional transport system consisting of 143 nodes, as shown in Figure 2.
## 2 Materials and Methods
### Bacteria amoA Gene Abundance  
Abundance of the bacterial gene _amo_A was determined using quantitative polymerase chain reaction (qPCR). The analysis was performed on a CFX96 Touch Real-Time PCR Detection System thermal cycler (Bio-Rad Laboratories, Inc., Hercules, CA, USA) using the primers provided by [PERSON] et al. [28]. The amplification reaction was carried out in a final volume of the reaction mixture of 20 uL, consisting of: 10 uL of Go-Taq(r) qPCR Master Mix (Promega), 0.5 uL of reverse and forward primer (10 uM), 0.5 uL of dimethyl sulfoxide (DMSO), 6 uL of PCR-grade water, and 2 uL of purified DNA (4 ng/uL). The operating conditions of the thermal cycler were as follows: polymerase activation at 95 \({}^{\circ}\)C for 2 min (1 cycle); then, 40 cycles: denaturation at 95 \({}^{\circ}\)C for 15 s, annealing, and extension 60 \({}^{\circ}\)C for 1 min. Melt curve analysis of the PCR product was performed (65-95 \({}^{\circ}\)C, 0.5 \({}^{\circ}\)C increments at 2-5 s/step) to confirm the specificity of the PCR product. The standard curve was generated according to the protocol provided by [PERSON] et al. [29]. The standard curve PCR product was purified using Wizard(r) SV Gel and PCR Clean-Up System (Promega). Sample and standard analyses were performed in triplicate; NTC (no-template control) also performed. The amplification efficiency was calculated according to the formula: E = [10\({}^{(-1/\mathrm{slope})}-1\)] \(\times\) 100, which in our study was 95%. The abundance of the bacterial _amo_A gene is expressed as log\({}_{10}\) gene copy number per soil dry weight (log\({}_{10}\) gene copies/g DW).
## 4 Computation and Communication Performance Modeling Approaches
### Computation Modeling Approach
#### 4.1.2 Data Dependency Consideration
This process also influences the way the JPEG decoder can be represented as SDF graph as shown in Figure 2c. After identifying and characterizing the different scenarios, we use Kernel Density Estimation (KDE) to increase the coverage of possible execution paths. This approach allows us to not only cover observed execution time during our simulation but also other delays that are likely to appear when using the actor with different stimuli. In the _third approach_ shown in Figure 3 (Many Paths), the actor is seen as a black box. Its execution time gets measured, but not assigned to any input data and therefore execution paths. This makes characterization more easy because no insight into the source code or knowledge about the algorithm is needed. The downside of this approach is that there is also no knowledge about the coverage of execution paths inside the actor. The _GetMCU_ actor used as example reads a Minimum Coded Unit of an JPEG encoded image. This included Run-Length and Huffman decoding which comes with a lot of possible execution paths. Instead of identifying and triggering each path with specific stimuli data, only a set of data that is representative for later use cases is used as input for an Actor. The Actor execution time gets then measured. The resulting observers execution times may only represent a subset of all possible execution paths. To address this issue we apply Kernel Density Estimation (KDE) to the measured delays, as we do in the second approach.