esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Experimental Design of Combined Remediation  
_Cynodon dactylon_ and _Bidentis pilosa_ had been selected as pioneer plants through pre-pot experiments. The solid waste material was a mixture of steel slag (SS), pyrolusite (PY) and ferrous sulfate (FS). The main components of SS and PY are shown in Tables 4 and 5. Among them, the amount of material 1 (M1) was 8% of that of soil (SS:PY:FS = 3:3:2), while that of material 2 (M2) was 11% (SS:PY:FS = 1:2:8). Polyethylene flowerpots (8.5 \(\times\) 10\({}^{-4}\) m\({}^{3}\)) were used for the experiment. Contaminated soil and solid waste materials were evenly mixed and put into flowerpots for one week. After stabilization, 0.2 g plant seeds were planted in the flowerpot. The group that only planted plants served as the control group. The combined remediation experiment was carried out in the greenhouse for 35 days. Then, plant and soil samples were sampled for further treatment. The detailed design of the experimental group is shown in Table 6, and each treatment group was repeated 3 times.  
\begin{table}
\begin{tabular}{c c c} \hline
**Plants** & **Family** & **Uses** \\ \hline _Cynodon dactylon_ & Poaceae & Landscape engineering, phytoextraction remediation \\ _Conyza canadensis_ & Asteraceae & Herb, horticulture, diet \\ _Praxelis demtidae_ & Asteraceae & Herb, horticulture, spice \\ _Bidentis pilosa_ & Asteraceae & Herb, horticulture \\ _Cucumis melo_ & Cucurbitaceae & Diet \\ _Capsicum annuum_ & Solanaceae & Diet \\ \hline \end{tabular}
\end{table}
Table 1: List of wild plants from the study site and the uses of those plants.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Properties** & **pH** & **SOM (g\(\cdot\)kg\({}^{-1}\))** & **AN (mg\(\cdot\)kg\({}^{-1}\))** & **AP (mg\(\cdot\)kg\({}^{-1}\))** & **AK (mg\(\cdot\)kg\({}^{-1}\))** & **CEC (meq\(\cdot\)100 g\({}^{-1}\))** \\ \hline Soil & 6.25 & 13.5 & 663 & 360 & 64 & 6.37 \\ \hline \end{tabular}
\end{table}
Table 2: Physical and chemical properties of soil samples.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Parameter** & **Cu** & **Pb** & **Zn** & **Cd** & **Cr** & **As** & **Ni** \\ \hline The total amount of heavy metals (mg/kg) & 13.31 & 11.94 & 55.02 & 0.38 & 70.31 & 76.14 & 13.38 \\ The bioavailable state content (mg/kg) & 1.71 & 3.18 & 6.80 & 0.25 & 0.14 & 0.13 & 0.13 \\ Pi values & 0.38 & 0.34 & 0.55 & 1.90 & 0.78 & 5.08 & 0.33 \\ Pollution level & Secure & Secure & Secure & Slight & Alert & Serious & Secure \\ \hline \end{tabular}
\end{table}
Table 3: The heavy metal content and pollution characteristics of soil.
## 5 Discussion and Conclusions  
In this paper, we introduced a new approach for nonlinear detection based on a SVM classifier. A simulator of the system with high data rate and high spectra efficiency was designed. We show that the performance can be significantly improved by using a linear SVM kernel for detection, which has less computational complexity and thus saves computation time and energy. Moreover, we only use four features for training and testing, which makes full use of the characteristics of SIF output signals and reduces the complexity significantly. Furthermore, we concentrated not only on reducing the BER of detection, but also on providing accurate PPEs. The BER performance was significantly improved for the SVM-LDPC compared with the MAPPE-LDPC approach. Also, the SVM method is more robust to sampling rate than the MAPPE method, and the former is proposed for use in detection when the sampling rate is low. In addition, we analyzed the SVM detector for a bandwidth efficient communication system with the NBPF added at the transmitting end. Such a system can meet the requirements of bandwidth limitation and achieve the desired performance. As a by-product, we have shown that the short bit duration (_i.e._, N = 5) makes the ISI, but the performance is almost identical when measured after the LDPC decoder. Also, the SVM probability output method does not need to estimate the channel noise power \(\sigma^{2}\), which reduces the complexity.  
In fact, the features selection procedure is somewhat elementary as an early work, and we haven't considered the resort to the feature combination technique which can reduce the corresponding problem dimensiond. If the optimal feature combination is used, accompanying the well-established features selection procedure, the gains achieved with this technique can be further enhanced, which also remains as an attractive area for future research.  
Figure 14: The BER performance at the channel decoder with SVM PEEs (dashed line) and with MAPPE (solid line). We represent the BER for \(f_{s}=4f_{c}\) with \(\lozenge,f_{s}=6f_{c}\) with *, and \(f_{s}=10f_{c}\) with \(\circ\), respectively.
## 4 Design Example and Simulation Results
### Voltage Regulation  
In any HESS, the supercapacitor is submitted to charge and discharging conditions. However, the supercapacitor charge (which is estimated from its voltage) must be limited and regulated to ensure the capability of providing and absorbing high-frequency transients. In fact, the \(v_{sc}\) profiles reported in Figures 15-17 show variations due to the bus current perturbations. This voltage variation is better observed in the simulation of Figure 18, where consecutive DC bus current perturbations are applied to demand energy from the supercapacitor: the bus current (\(i_{dc}\)) starts from \(-3\) A, increasing in \(1\) A each \(4\) s to reach \(3\) A. This test shows the need of a voltage controller for \(v_{sc}\); otherwise, the supercapacitor voltage decreases as it delivers energy to support the positive high-frequency transients. On the other hand, if negative high-frequency transients are consecutively applied, \(v_{sc}\) will reach an overvoltage condition, which could damage the supercapacitor.  
Therefore, the external voltage loop is activated for the following tests, which consider the same persistent current profile used in the previous simulation of Figure 18. The simulations including the external voltage loop are reported in Figures 19-21, where the control of the supercapacitor voltage is evaluated at operating points equal to \(9\) V, \(12\) V, and \(15\) V, respectively.  
Figure 16: Performance of the LQG-FF controller with \(v_{sc}=12\) V and \(v_{dc}=12\) V (unitary gain).  
Figure 17: Performance of the LQG-FF controller with \(v_{sc}=15\) V and \(v_{dc}=12\) V (buck mode).  
The new simulation results, reported in Figures 19-21, show the correct regulation of the supercapacitor voltage (\(v_{sc}\)) (and SoC), which in all cases exhibits a null steady-state error. The transient response in \(v_{sc}\) has the same dynamic behavior as the duty cycle, which is forced to provide the current transient provided by the supercapacitor. Finally, the current transient is also observed in the \(v_{sc}\)-\(v_{dc}\) plane.  
Figure 19: Performance of voltage–current control LQG-FF with \(v_{sc}=9\) V and \(v_{dc}=12\) V (boost mode).  
Figure 20: Performance of voltage–current control LQG-FF with \(v_{sc}=12\) V and \(v_{dc}=12\) V (unitary gain mode).  
Figure 18: Performance of current control LQG-FF with \(v_{sc}=12\) V and \(v_{dc}=12\) V (unitary gain).  
the adaptive gain-scheduling strategy adjusts the controller parameters to ensure the same dynamic performance in all the operating points, and at the same time prevent the duty cycle saturation.  
In conclusion, the simulations reported in this section confirm the correct operation of the proposed HESS and control system. In particular, the HESS ensures that high-frequency transients are provided by the supercapacitor, while low-frequency transients are provided by the battery, thus reducing battery stress. In addition, the supercapacitor voltage is controlled, thus regulating the SoC, to ensure the HESS capability to provide or absorb load transients.
# BL0K: A New Stage of Privacy-Preserving Scope for Location-Based Services
## 7 Performance Results
### Computing Performance Measurement  
Regarding computing performance measurement, the average of the BLOT computation time measured in the simulation process was about 10 ms. The BL0K shows an average calculating time of 7.8 ms. Both of BL0K and BLOT were measured at 2.5 GHz with a RAM of 4 GB.
## 2 General CFD Software Based on Finite Volume Method
### Parallel Solving Algorithm
#### 2.2.3 Parallel Architecture Based on MPI and OpenMP  
Based on the grid model decomposition described above, two or more levels of data partitioning can be carried out. Depending on the computational resources or data structure, a certain number of \"sub-grids\" can be used as input to a process to perform computation. A hybrid MPI/OpenMP parallelism mode can be used. MPI parallelism is used on the first-level partition, and then the first-level partition is divided into several second-level sub-partitions. The number of second-level sub-partitions is generally equivalent to the cores inside the compute node. Coarse-grained OpenMP parallelism is performed on the second-level partition.  
This multi-tier parallel architecture is adapted to the current architecture of supercomputer clusters, where primary partitions are assigned to individual compute nodes, while secondary partitions are on top of the same compute node. The above is also applicable to heterogeneous systems. The first layer still uses MPI communication based on the grid's first-level partition interfacing information.The second layer is OpenMP parallelism, i.e., coarse-grained parallelism between compute cores, based on secondary subpartitions of the grid. Data transfer between CPUs and coprocessors is performed using the corresponding channels. When there is no coprocessor, the above heterogeneous parallel mode automatically degenerates to homogeneous parallel mode. In order to provide better control over the different partitioning layers, the whole computational domain is divided into two layers: Layer 1 is the **Region** partition and Layer 2 is the **Zone** sub-partition. In parallel computation, the **Region** partition is assigned to a compute node, corresponding to an MPI process, and the **Zone** subpartition is assigned to a compute core thread, corresponding to an OpenMP thread.  
A typical CFD solver execution process mainly includes residual initialization, time step calculation, flux calculation, time advancement, flow field update, and intersection communication. The solver is applied to the computational domain **Zone**. For different physical models, multiple solvers may be loaded on a **Zone**. During the computation, **Region** traverses each of these **Zones** in turn, calling the corresponding solver to perform the computational task. When OpenMP parallelism is used, the computational tasks can be performed concurrently for each **Zone**.
## 3 Results and Discussion
### Construction of a Genomic Library and Screening of Degrading Genes  
Pyrethroids are ester-containing compounds. As suggested by the degradation pathway, the cleavage of ester bonds by esterase is the major route of degradation [21]. The genomic library of _B. cereus_ BCC01 was constructed using cloning vector pUC18, in which approximately 11,000 clones were generated. After two rounds of screening, one recombinant demonstrated high activity, such that 46.8% of the spiked beta-cypermethrin was efficiently degraded within five days of incubation, while only 9% was metabolized in the control, in line with a previous study [30]. The low yield of identification may be partly due to the limit of quality of the genomic library we constructed, the probable unrecognition of regulatory elements from unknown bacteria in _E. coli_, difficulties of expression in heterologous host or the formation of inclusion bodies and the toxicity of the expressed proteins to hosts [37]. Sequence analysis indicated that the inserted fragment was 1492 bp (accession number: MH58868), containing a 744 bp ORF, and showing a highly conserved domain (with 99% of similarity) to the reported gene _estA_ encoding pyrethroid-hydrolyzing esterase from _B. cereus_ E33L  
Figure 2: The growth of _Bacillus cereus_ BCC01 and degradation of beta-cypermethrin. (**A**) The growth curves of strain BCC01. \(\bullet\), LB media; \(\blacktriangle\), with 100 mg/L Beta-cypermethrin in LB media. (**B**) Bacterial growth and related degradation of beta-cypermethrin. \(\bullet\), beta-cypermethrin control without bacteria; \(\blacktriangle\), growth in 100 mg/L beta-cypermethrin; and \(\blacksquare\), degradation of beta-cypermethrin with _Bacillus cereus_ BCC01.  
(accession no., CP009968.1). Therefore, the esterase gene that was screened out from this study was named _estA_. Subsequently, bioinformatics analysis indicated that the protein EstA was composed of 247 amino acids and its molecular mass was 28.2 kDa with pI at 5.2. It displayed as 45.3% \(\alpha\)-helix, 15% \(\beta\)-sheet, and 39.7% random coil in its secondary structure (Figure S2). The data further confirmed that there was a conversed domain of esterase/lipase. Compared with multiple esterase sequences, the Ser94 of EstA was located in a highly conserved esterase peptide motif of Gly-X-Ser-X-Gly (Figure S3), forming a concave active center for the biodegradation of beta-cypermethrin, which is a typical feature of the esterase family [38; 39]. As reported previously, the carboxylase from _Breviobacillus_ sp. strain KB2 and _B. cereus_ strain PU was be able to degrade malathion, and the carboxylase gene was cloned [40; 41], however, the ability of EstA to degrade pyrethroid pesticide was never reported.
## 3 Analysis of the Onsite Monitoring Data
### Monitoring of Horizontal Soil Displacement
The horizontal displacement increases during the two pause stages, which lasted 10 days and 6 days and accounted for 27.25% and 27.44%, respectively, of the total horizontal displacement. This finding indicates that, as the construction depth increases, the construction pauses affect horizontal displacement, although the influence is less significant than that of settlement deformation. Given the large excavation depth of this shaft, particular attention should be given to controlling horizontal deformation. Therefore, point CX-4, which is located deeper, was selected to analyze the horizontal displacement of the surrounding soil. Figure 9 shows the development of horizontal displacement at CX-4 during the pit bottom construction period. Excessive horizontal displacement was observed in the portion below the excavation bottom. During shaft construction in soft soil areas, as the soil is excavated layer by layer, the soil stress is gradually released, triggering horizontal displacement. Owing to the weakening of the shaft bottom support, localized displacements occurred in the soil at the edge of the shaft bottom. During the bottom clearing phase of the shaft construction, significant heating at the bottom of the shaft was observed on-site. Bottom heave is the result of multiple factors  
Figure 9: Horizontal displacement development curve of CX-4 during pit bottom construction. acting together [26]. In the VSM sinking process, since dewatering techniques are not used and there is no underground diaphragm wall constraint, the heave of the soil is mainly due to the excavation unloading. Excavation within the shaft leads to the vertical rebound deformation of the surrounding soil, while the redistribution of external soil stresses causes the soil to move towards the inner side of the shaft, thus forming the bottom heave [17]. The settlement and horizontal displacement generated during shaft sinking are primarily due to the disturbances caused by construction and the creep effects during the pause periods. To mitigate the increase in settlement, the construction schedule should be reasonably arranged, and prolonged pauses should be avoided. During the pause, the water level in the shaft can be appropriately increased. The rise of the water level will increase the pore water pressure of the soil, reduce the effective stress of the soil, reduce the consolidation deformation of the soil, and inhibit the creep of the soil. To reduce the increase in horizontal displacement, the concentration of bentonite slurry can be adjusted to modify the strength and stiffness of the grouting layer. This allows the bentonite to permeate the soil's voids, enhancing the integrity of the grouting layer and effectively reducing the horizontal displacement of the soil [18].
# Long-Distance Shield Tunnelling Performance Prediction Based on Informer
## 2 Methodology
### LSPP Model Design
#### 2.2.2 Data Preparation
Data standardization  
During the model training process, the different dimensions and magnitudes of the input features will affect the training effect [39]. The data are normalized by the Z-score \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Type** & **Full Name** & **Variable** & **Unit** \\ \hline \multirow{4}{*}{Input} & \multirow{4}{*}{Tunnelling parameters} & \multicolumn{2}{c}{Cutterhead rotation speed} & \(CS\) & RPM \\  & & \multicolumn{2}{c}{Cutterhead torque} & \(CT\) & KN\(\cdot\)m \\  & & Earth pressure & \(EP\) & MPa \\  & & Screw conveyor speed & \(SS\) & RPM \\  & & \multicolumn{2}{c}{Cutterhead current} & \(CC\) & A \\  & & \multicolumn{2}{c}{Specific energy} & \(SE\) & MJ/m\({}^{3}\) \\ \cline{2-5}  & \multicolumn{2}{c}{Geological conditions} & \multicolumn{2}{c}{Cover depth} & \(CD\) & m \\  & & \multicolumn{2}{c}{Earth type} & \(ET\) & - \\ \hline \multirow{2}{*}{Output} & \multicolumn{2}{c}{Tunnelling parameters} & \multicolumn{2}{c}{Penetration rate} & \(PR\) & mm/rev \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Input and output parameters of the LSPP model. normalization method, as shown in Equation (7), which eliminates the potential bias caused by the dimension difference. \[Z=\frac{X-\mu}{\sigma} \tag{7}\]  
where \(Z\) represents the standardized data, \(X\) represents the sample value of the excavation feature, \(\mu\) represents the mean corresponding to the sample feature, and \(\sigma\) represents the standard deviation corresponding to the excavation feature \(X\). 4. Spatial alignment of data  
Due to the sparseness of the geologic information of the construction site compared with the tunnel construction data, we calculated the geological data surrounding the borehole using Inverse Distance Weighting (IDW) [40] in conjunction with the geological exploration data to determine the soil information of each segment ring. The determination of the weight of the influence of the sample point on the value to be interpolated is achieved by calculating the Euclidean distance between the sample point and the value to be interpolated. The weights of the sample points are calculated according to Equation (8). \[w_{i}=\frac{1}{d_{i}^{2}} \tag{8}\]  
where \(w_{i}\) represents the weight of the \(i\)-th sample point and \(d_{i}\) represents the distance between the sample point and the position to be interpolated. The interpolation position is aligned with the mileage required for calculation, ensuring the correct association between the formation information and the excavation parameters.
# Soft Decision Decoding with Cyclic Information Set and the Decoder Architecture for Cyclic Codes
###### Abstract  
The soft decision decoding algorithm for cyclic codes, especially the maximum likelihood (ML) decoding algorithm, can obtain significant performance superior to that of algebraic decoding, but the complexity is much higher. To deal with this problem, an improved soft decision decoding algorithm based on a cyclic information set and its efficient implementation architecture are proposed. This algorithm employs the property of the cyclic codes to generate a series of cyclic information sequences by circularly shifting, constructing the cyclic information set. Then, a limited number of candidate information sequences are efficiently generated using an iterative computation method, and the candidate codewords are generated using the very concise encoding method of the cyclic codes. Furthermore, the efficient hardware architecture based on systolic arrays is also proposed to generate candidate information sequences and to select the optimal candidate codewords. An emulation platform is constructed to verify the error correction performance and to determine the optimal decoder parameters. Emulation results indicate that, with appropriate parameter selection, the proposed decoding algorithm can achieve a bit error rate approaching the ML performance while maintaining low complexity.  
soft decision decoding; cyclic codes; information sets; decoder architecture
## 4 Conclusions  
In this study, the effect of extended ball-milling on the structural, morphological and optical properties of MAPbI\({}_{3}\) 3D and PEA\({}_{2}\)PbI\({}_{4}\) 2D perovskite crystals was investigated. Particularly, polycrystalline powders were synthesized via a wet-chemistry method; they then underwent high-energy ball-millings of different durations, and were analyzed using XRD, SEM, absorbance and PL spectroscopies. From the structural point of view, the XRD results showed no sign of amorphization for either sample, even after the prolonged milling treatment. Additionally, the processing induces a clear phase transition (from tetragonal to cubic) already after 72 h for MAPbI\({}_{3}\), and a significant crystallinity reduction for the PEA\({}_{2}\)PbI\({}_{4}\) perovskite. The morphological SEM investigation displayed a reduction of the crystals' quality due to the ball-milling, with a reduction of their size and an agglomeration tendency. The optical characterization showed an interesting effect on the MAPbI\({}_{3}\) and PEA\({}_{2}\)PbI\({}_{4}\)'s band gap tunings, attributable to the high-energy treatment. Additionally, the 2D perovskite exhibited a reduction in terms of emission intensity and lifetime, probably due to the introduction of electronic defects, attributable the decreased quality of the crystals.  
In conclusion, the mechanical ball-milling process affects many properties of crystalline MAPbI\({}_{3}\) 3D and PEA\({}_{2}\)PbI\({}_{4}\) 2D perovskites, without incurring their amorphization, and it could be further applied to a wider class of OHPs in order to gain information regarding (micro)structure-properties correlations.  
Conceptualization, L.M., A.L.; methodology and investigation, S.B., V.A., S.C., G.A.; data curation and validation, L.M., A.L.; visualization, S.B., S.C., A.R., G.A.; writing--original draft preparation, V.A., A.L., L.M.; writing--review and editing, A.L., M., A.R., S.C., G.A.; supervision, resources and funding acquisition, L.M., F.F., A.R. All authors have read and agreed to the published version of the manuscript.  
This research was funded by PON Project \"Tecnologia per celle solari bifacciali ad alta Efficienza a 4 terminali per utility scale\" (BEST-4U), of the Italian Ministry MIUR (CUP B88D19000160005)\".  
[PERSON] is acknowledged for SEM data collection.  
The authors declare no conflict of interest.
# Extended SSH Model in Non-Hermitian Waveguides with Alternating Real and Imaginary Couplings
###### Abstract  
We studied the topological properties of an extended Su-Schrieffer-Heeger (SSH) model composed of a binary waveguide array with alternating real and imaginary couplings. The topological invariant of the periodic structures remained quantized with chiral symmetry even though the system was non-Hermitian. The numerical results indicated that phase transition arose when the absolute values of the two couplings were equal. The system supported a topological zero mode at the boundary of nontrivial structures when chiral symmetry was preserved. By adding onsite gain and loss to break chiral symmetry, the topological modes dominated in all supermodes with maximum absolute value of imaginary energy. This study enriches research on the SSH model in non-Hermitian systems and may find applications in optical routers and switches.  
topological phase; waveguides; non-Hermitian optics Article