esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
According to the population analysis of functional bacteria, SRB decreased by 2 orders of magnitude in W226, compared to that in W202 at day 30. Moreover, _Desulfovibrus_ and _Desulfovcurus_ were predominant in control well W202, except for _Arcobacter_, as its number decreased sharply after nutrient injection. A similar trend was also observed in the case of the other SRB, such as _Desulfovarbo_ and _Desulfoglaeba_. During the process of growth and metabolism, SRB populations usually utilize sulphate ions as a terminal electron acceptor to generate acidic H\({}_{2}\)S gas, causing biosouring in the system. Furthermore, the H\({}_{2}\)S produced can have an adverse impact on subsequent processes, such as reduce the oil quality and cause the corrosion of metal pipelines and oil storage tanks used in the exploitation and transportation of crude oil [25,45]. One of the potential approaches to reduce the adverse effect of SRB is an increase in the growth of NRB [46,47]. Heterotrophic NRB can compete with SRB for electron donors in the system, thus inhibiting the growth of SRB. According to an analysis of functional bacteria, NRB shows an increase in W226 compared to that in W202, which is in accordance with the hypothesis that NRB compete for electron donors with SRB and inhibit the growth of SRB in oil reservoirs. Therefore, in this study, the injection of nutrients effectively inhibited the growth of SRBs, which contributed to the improvement of oil recovery. The results of this study have provided several interesting insights into MEOR by nutrient stimulation. First, the components in the injected nutrients can be detected in the production water of well 226, indicating that the nutrient has entered the target block. However, due to the severe horizontal and vertical heterogeneity of the tested oil reservoirs and the low viscosity of the nutrient mixture, the contact time of the nutrients with the target microorganisms may be limited, which may restrict the growth of functional microbes. Second, on day 30 of nutrient flooding, the indigenous functional bacteria were activated. The abundance of functional bacteria, HOB and NRB, exhibited a significant increase at day 75, revealing successful selective stimulation. One of the main mechanisms of nutrient flooding is to activate microorganisms that are capable of biosurfactant production, which can also be used as indigenous nutrients for the metabolism and reproduction of microorganisms in situ [16]. Therefore, the combined technology of nutrient and biosurfactants should  
Figure 6: Variation in predicted genes from bacteria (**a**) and archaea (**b**) in production and control well at the KEGG level 2 categories by PICRUSt analysis. be studied to strengthen the activation effect of indigenous functional microorganisms and improve the properties of the oil/water/rock interface to improve and prolong the effectiveness and effective period of MEOR.
# Tree Based Approaches for Predicting Concrete Carbonation Coefficient
## 5 Conclusions  
The present investigation constitutes a first approach towards modelling concrete carbonation using three data-driven techniques: Model Tree (MT), Random Forest (RF) and Multigene Genetic Programming (MGGP). Three models to predict concrete carbonation coefficient were developed and analysed, using MT, RF and MGGP. The performance of these models was compared with assembled MLR models, ANNs and GP models, which were previously applied in this field. In the different models proposed, MT, RF and MGGP have shown good performance in predicting the concrete carbonation coefficient. Further, these models revealed similar performance between them and an enhanced performance when compared with MLR, ANN and GP.  
In all the models discussed, RF on average scores well on performance, as it leverages the power of multiple decision trees and it does not rely on the feature importance given by a single decision tree. MT coupled with the divide and conquer technique acquires the advantage of splitting until the lowest standard deviation error is reached and thus contributes to the good performance of models rather than a single tree. On the other hand, MGGP, with its evolution structure and operators such as reproduction, cross-over and mutation, offers the advantage of the selection of influential parameters deriving equations that lower the mean squared error. The weighted linear combination number of genes in MGGP helps to obtain better performance. Parameter influence in MT in the form of coefficients, permutation importance in RF, input frequency in MGGP and coefficients in MLR facilitate an understanding of the influential parameters, and thus we attempt to shatter the myth that these DDTs are \"black boxes\" and can be at least labelled as \"grey boxes\".  
This work also attempts to provide the readers with a tool in the form of series of equations that can be easily used through MT; while RF with series of trees can be combined further for easy deployment. MGGP presents a single equation that can be easily deployed and is user-friendly.  
Conceptualisation, A.S. and R.N.; methodology, S.L., P.K., P.D.; software, S.L., P.K., P.D.; validation, S.L., P.K., P.D.; formal analysis, A.S., R.N. and J.d.B.; writing--original draft preparation, S.L., P.K., P.D.; writing--review and editing, A.S., R.N. and J.d.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by FCT (Portuguese Foundation for Science and Technology) through the project BestMaintenance-LowerRisks (PTDC/ECI-CON/29286/2017).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors gratefully acknowledge the support of the Vishwakarma Institute of Information Technology (Pune, India) and to the CERIS Research Centre (Instituto Superior Tecnico-University of Lisbon).  
The authors declare no conflict of interest.
## 2 Methods and Materials
### Experimental Analysis
#### 2.2.3 DNA Extraction, Illumina MiSeq Sequencing, and Real-Time PCR of _cbbL_ Gene  
For analyzing the composition changes of the CO\({}_{2}\)-assimilating bacterial communities during the glucose degradation, sediment samples from the 8 th, 16 th, 24 th, and 45 th hour were selected for DNA extraction and the quantification of the cbbL gene in the four pots [26]. The freeze-dried samples were weighed at 0.5000 g, and a bacterial DNA isolation kit (TianGEN Biotech Co. Ltd., Beijing, China) and Nanodrop were used to extract and quantify the DNA, respectively. The quality of the extracted DNA was tested by 1.2% agarose gel electrophoresis. K2f (5\({}^{\prime}\)-ACCAYCAAGCCSAAGCTSSGG-3\({}^{\prime}\)) and V2f (5\({}^{\prime}\)-GCCTTCSAGCTTGCCSACCRC-3\({}^{\prime}\)) were the primers of the _cbbL_ gene [33]. Then, PCR amplification was conducted based on the primers. Magnetic beads (Vayzyme VAHTSTM DNA Clean Beads) were used to recover the purified PCR product. In addition, the Quant-iT PicoGreen dsDNA Assay Kit reagent and a microplate reader (BioTek, FLx800) were used to fluorescently quantify the purified PCR product [34]. The cycle parameters were as follows: 5 min at 95 \({}^{\circ}\)C, 40 cycles of 15 s min at 95 \({}^{\circ}\)C and 30 s at 60 \({}^{\circ}\)C, and 1 min 30 s at 72 \({}^{\circ}\)C. These cycles were followed by 10 min incubation at 70 \({}^{\circ}\)C. A TruSeq Nano DNA LT Library Prep Kit (Illumina Company, LTD) was applied to construct the sequencing library, which was qualified and tested with an Agilent Bioanalyzer and with an Agilent High Sensitivity DNA Kit. The sequencing length of the target fragment was 400-450 bp. The sequencing library was quantified with a Promega Quanti-Fluor system and a Quant-iT PicoGreen dsDNA Assay Kit. Paired-end sequencing was carried out using a MiSeq Reagent Kit V3 (600 cycles) and MiSeq sequenator [23]. During the process, the raw sequencing of high throughput was preliminarily screened, and barcode sequencing was removed. The analysis procedures of QIIME 2 and Vsearch software were used to denoise and cluster the operational taxonomic units (OTUs).
# Collective Communication Performance Evaluation for Distributed Deep Learning Training
## 6 Experiments in the PyTorch Environment
### All-Reduce Routine  
In Figure 21, the results of the ring all-reduce routine are depicted. When examining the MPI_Allreduce measurements, a significant difference in latency is evident between bare metal and the singularity environment. Specifically, with three GPUs in use, singularity takes 33% less time than bare metal. For GLOO_Allreduce, there is not a substantial difference in latency across all environments. Notably, when comparing cross docker with other environments, cross docker shows only a 2-7% difference in latency. In the case of NCCL_Allreduce, it demonstrates strong performance compared to other backend architectures. On bare metal with four GPUs, NCCL recorded a latency of 0.6470 s, which is 332% longer than MPI and 149% longer than GLOO. However, when comparing cross docker to the other NCCL environments, a significant difference in latency is noticeable. When using four GPUs, cross docker exhibits a considerably higher latency measurement: 129% higher compared to bare metal. This suggests that NCCL's performance is significantly impacted by the configuration and scaling of GPUs in a cross-docker environment.
## 3 Results and Discussion
Its ability to generalize under uncertainty, and maintain physical consistency and scale across different network sizes suggests strong potential for deployment in modern grid monitoring and control systems--particularly in settings where measurement infrastructure is sparse or noisy. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Drop Out (\%)**} & \multirow{2}{*}{**Network**} & \multicolumn{2}{c}{**Baseline**} & \multicolumn{2}{c}{**PINN**} & \multicolumn{2}{c}{**Improvement (\%)**} \\ \cline{3-8}  & & \multicolumn{2}{c}{**MAE**} & \multicolumn{2}{c}{**MAE**} & \multicolumn{2}{c}{**MAE**} \\ \cline{3-8}  & & **V** & **I** & **V** & **I** & **V** & **I** \\ \hline \multirow{8}{*}{**0**} & IEEE13 & 0.26 & 23.44 & 0.17 & 0.60 & 35.13 & 97.43 \\  & IEEE34 & 1.30 & 16.78 & 1.18 & 2.93 & 9.83 & 82.56 \\  & IEEE37 & 0.78 & 7.27 & 0.22 & 0.42 & 72.27 & 94.22 \\  & IEEE123 & 0.14 & 5.32 & 0.13 & 0.44 & 7.05 & 91.82 \\ \hline \multirow{8}{*}{**10**} & IEEE13 & 17.14 & 370.93 & 20.25 & 29.11 & \(-\)18.13 & 92.15 \\  & IEEE34 & 113.53 & 185.43 & 112.42 & 52.19 & 0.98 & 71.85 \\  & IEEE37 & 20.84 & 45.31 & 22.17 & 8.38 & \(-\)6.34 & 81.50 \\  & IEEE123 & 9.65 & 20.70 & 9.53 & 2.79 & 1.26 & 86.50 \\ \hline \multirow{8}{*}{**20**} & IEEE13 & 22.49 & 467.47 & 28.03 & 42.08 & \(-\)24.67 & 91.00 \\  & IEEE34 & 143.65 & 228.49 & 139.73 & 65.94 & 2.73 & 71.14 \\  & IEEE37 & 30.65 & 52.01 & 33.07 & 9.69 & \(-\)7.87 & 81.36 \\  & IEEE123 & 12.27 & 28.25 & 12.38 & 3.85 & \(-\)0.92 & 86.36 \\ \hline \multirow{8}{*}{**10**} & \multicolumn{2}{c}{**RMSE**} & \multicolumn{2}{c}{**RMSE**} & \multicolumn{2}{c}{**RMSE**} \\ \cline{3-8}  & & **V** & **I** & **V** & **I** & **V** & **I** \\ \cline{3-8}  & IEEE13 & 0.39 & 50.94 & 0.25 & 1.08 & 35.63 & 97.89 \\  & IEEE34 & 1.90 & 50.94 & 1.70 & 7.32 & 10.14 & 85.64 \\  & IEEE37 & 0.97 & 26.26 & 0.28 & 0.54
## 2 Design and Optimization of Proposed Unit Cell
### Design of Proposed Unit Cell Structure
The design principle of the unit cell is incorporated to achieve wideband resonance characteristics and one-bit phase shift control characteristic. The proposed unit cell utilizes a meander shape, re-resonating parasitic patches, and dual hexagonal impedance-matching patches. The hexagonal structures resonate on higher resonance modes with primary as circular mode. Higher resonance modes can be tuned with the lower resonance mode to enhance the bandwidth of the acquire layer. The transmitter-layer hexagonal patch is engraved with meander shapes to improve the structure's bandwidth capability. These are designed based on the linear equation \(d_{1}=k\times d_{2}\), where \(d_{2}\) is the diameter of the outer-ring meanders of the hexagonal angular patch and \(d_{1}\) is the diameter of an inner-ring meander of the hexagonal angular patch of the transmitter layer. \(k\) is the meander constant,  
Figure 1: Three-dimensional exploded view: (**a**) top view, (**b**) bias circuit, (**c**) ground, (**d**) receiver layer, (**e**) impedance-matching receiving layer. which can vary from 0.2 mm to 0.9 mm, whereas \(k=0.3\) mm is used in this structure as the optimization meander constant value of the structure. The 4-edge parasitic patches are used to re-resonate the resonating antenna's EM field to enhance the desired range of frequencies of more than 500 MHz. The E-field of the resonating patch at 16.5 GHz is demonstrated in Figure 2 to understate the phase state control operation of the proposed unit cell. The propagation pathway of state I and state II are reversed. The phase shift control of the unit cell is attained by changing the propagation path in the mirror direction under the current path reverse direction phenomenon. The radiation layer of the transmission array element adopts the cascade structure design. The lowermost receiver and impedance-matching layer both form a cascade from the receiver layer of the unit cell to realize EM waves from the feed source. The structure expands the bandwidth and can be used as a wide-angle impedance-matching layer to improve the beam-scanning characteristics of the transmitray. The layer's offset distance relative to the substrate's center (along the \(y\)-axis) is d, and the y-polarization mode radiates the electromagnetic wave.
## 4 Simulation and Results
Given this setup, the bound on the collision probability (\(P_{Collision}\)) for subblock confirmation with a hash value length \(l_{hash}\) of 64 bits and a subblock \(N_{sb}\) of 16,200 bits is \(P_{Collision}\leq 7.11\times 10^{-12}\) per subblock. In Figure 8, the performance of the proposed rate-adaptive reconciliation with syndrome-based QBER estimation and subblock confirmation is presented by simulating (a) the secret key rate as a function of the quantum bit error rate (QBER) and (b) the secret key throughput as a function of the distance (km) over the quantum channel for a QKD system operating at a 1 GHz clock rate. These results  
Figure 7: Comparison of (**a**) the success rate (%) and (**b**) the frame error rate (FER) between the proposed rate-adaptive reconciliation performed solely in the primary round, and its integration with subblock confirmation through an iterative process in the additional rounds. These simulation results are obtained from 2000 iterations for every observed QBER value. properties of the single photon source and detection, as well as the optical fiber losses of the quantum channel for a BB84 QKD protocol, using the parameters listed in Table 4. The proposed scheme achieves the secret key rate and throughput, approaching the theoretical limit of the perfect information reconciliation, where \(\eta_{IR}=1\) and FER = 0. It outperforms other methods, such as Cascade, blind reconciliation, and symmetric blind reconciliation. The curve representing perfect information reconciliation, as depicted in Figure 8b, indicates a drop in secret key throughput to 1 Kbps at a transmission distance of approximately 49.85 km. In comparison, the curve for the proposed rate-adaptive reconciliation with integrated syndrome-based estimation and subblock confirmation achieves a throughput of 1 Kbps at a distance of approximately 49.10 km, closely approaching the ideal performance of perfect information reconciliation. Meanwhile, symmetric blind reconciliation, Cascade, and blind reconciliation reach their maximum distances for a throughput of 1 Kbps at approximately 48.70 km, 48.50 km, and 47.15 km, respectively. From these performance evaluations, the unified approach of the proposed scheme significantly improves the achievability of higher secret key throughput over longer transmission distances. Figure 8: Comparison of (**a**) the secret key rate as a function of the quantum bit error rate (QBER) and (**b**) the secret key throughput as a function of the distance over the quantum channel (km) with a QKD system operating at 1 GHz clock rate, utilizing the parameters of a BB84 QKD system as defined in Table 4. In these simulations, the proposed rate-adaptive reconciliation with syndrome-based estimation and subblock confirmation is evaluated and compared with Cascade, blind, and symmetric blind reconciliation.
# A Simplified Matrix Formulation for Sensitivity Analysis of Hidden Markov Models
## 3 Sensitivity Analysis of Filtering Probability
### Initial Parameter Variation
#### 3.2.2 SMF for Sensitivity Analysis of the Filtering Probability to the Initial Parameter Variation  
In the simplified matrix representation, the sensitivity function \(p(x_{v}^{t},\mathbf{y_{e}^{1:t}})(\theta_{\gamma})\) shown in Equation (8) can be written as a simple matrix-vector multiplication. It follows that for \(t=1\) we have the constant \(p(x_{v}^{1},\mathbf{y_{e}^{1}})(\theta_{\gamma})=o_{v,\mathbf{e}^{1}}\cdot \gamma_{v}(\theta_{\gamma})\), which becomes \(p(X^{1},\mathbf{y_{e}^{1}})(\theta_{\gamma})=O^{1}\Gamma^{\prime}(\theta_{ \gamma})\) in the simplified matrix representation. For \(t>1\), we have  
\[p(x_{v}^{t},\mathbf{y_{e}^{1:t}})(\theta_{\gamma})=o_{v,\mathbf{e}^{1}}\cdot \sum_{z=1}^{n}a_{z,v}\cdot p(x_{v}^{t-1},\mathbf{y_{e}^{1:t-1}})(\theta_{ \gamma})\]  
which is represented as  
\[p(X^{t},\mathbf{y_{e}^{1:t}})(\theta_{\gamma})=O^{t}A^{\intercal}p(X^{t-1}, \mathbf{y_{e}^{1:t-1}})(\theta_{\gamma})\]  
To solve the forward (filtering) probability sensitivity function \(p(x_{v}^{t},\mathbf{y_{e}^{1:t}})(\theta_{\gamma})\), the following algorithm implementation is presented.
# The Impact of the 4.0 Paradigm in the Italian Agricultural Sector: A Descriptive Survey
## 2 Literature Review  
### Agri 4.0: Phenomenon and Paradigm Definition  
The concept of Agri 4.0 encompasses several different scientific domains, some of which are directly related to land cultivation (water control, crop cultivation, harvesting, etc.), while others are an expansion of the agricultural area to different disciplines, such as engineering, economics, management, and so forth. Advances in different areas of the information and communication technology (ICT) domain, combined with the need to improve agricultural productivity, both for food safety and environmental impact issues, have determined the research area for Agri 4.0. Therefore, Agri 4.0 is derived from the broader concept of Industry 4.0 [9], which aims to define the integration of different technologies (such as Internet of Things (IoT), artificial intelligence, cloud computing, etc.) to automate cyber-physical tasks and processes, allowing for better planning and control of agricultural systems. The relationship of this concept with that of the Industry 4.0 paradigm, that is, the adoption of digital technologies to support the processes of manufacturing companies, is clear.  
As reported in the literature, reducing input costs and increasing productivity seem to be the driving forces behind the progress in agriculture. However, the importance of sustainability should not be overlooked, a concept that has emerged as a major issue across the spectrum of human activities. Therefore, one of the goals of Agri 4.0 is to minimize the environmental impact of agricultural activities [14]. Thus, the implementation of Agri 4.0 solutions implies the possibility of farms achieving certain goals and benefits.
## 2 Background
### ECC over \(GF(2^{m})\)
```
1 Input:\(k=(k_{n-1},\ldots,k_{1},k_{0})\) with \(k_{n-1}=1\), \(P=(x_{p},y_{p})\in GF(2^{m})\)
2 Output:\(Q=(x_{q},y_{q})=k\cdot P\)
3 Set\(X_{1}=x_{p}\), \(Z_{1}=1\), \(Z_{2}=x_{p}^{2}\) and \(X_{2}=x_{p}^{4}+b\)
4 for(i from \(m-2\) down to 0)do
5 if(\(k_{i}=1\))then
6\(Inst_{1}\gets Z_{1}=X_{2}\times Z_{1}\)
7\(Inst_{2}\gets X_{1}=X_{1}\times Z_{2}\)
8\(Inst_{3}\gets T_{1}=X_{1}+Z_{1}\)
9\(Inst_{4}\gets X_{1}=X_{1}\times Z_{1}\)
10\(Inst_{5}\gets Z_{1}=T_{1}^{2}\)
11\(Inst_{6}\gets T_{1}=x_{p}\times Z_{1}\)
12\(Inst_{7}\gets X_{1}=X_{1}+T_{1}\)
13\(Inst_{8}\gets Z_{2}=Z_{2}^{2}\)
14\(Inst_{9}\gets T_{1}=Z_{2}^{2}\)
15\(Inst_{10}\gets T_{1}=b\times T_{1}\)
16\(Inst_{11}\gets X_{2}=X_{2}^{2}\)
17\(Inst_{12}\gets Z_{2}=X_{2}\times Z_{2}\)
18\(Inst_{13}\gets X_{2}=X_{2}^{2}\)
19\(Inst_{14}\gets X_{2}=X_{2}+T_{1}\)
20 else
21\(Inst_{1}\gets Z_{2}=X_{1}\times Z_{2}\)
22\(Inst_{2}\gets X_{2}=X_{2}\times Z_{1}\)
23\(Inst_{3}\gets T_{1}=X_{2}+Z_{2}\)
24\(Inst_{4}\gets X_{2}=X_{2}\times Z_{2}\)
25\(Inst_{5}\gets Z_{2}=T_{1}^{1}\)
26\(Inst_{6}\gets T_{1}=x_{p}\times Z_{2}\)
27\(Inst_{7}\gets X_{2}=X_{2}+T_{1}\)
28\(Inst_{8}\gets Z_{1}=Z_{1}^{2}\)
29\(Inst_{9}\gets T_{1}=Z_{1}^{2}\)
30\(Inst_{10}\gets T_{1}=b\times T_{1}\)
31\(Inst_{11}\gets X_{1}=X_{1}^{2}\)
32\(Inst_{12}\gets Z_{1}=X_{1}\times Z_{1}\)
33\(Inst_{13}\gets X_{1}=X_{1}^{2}\)
34\(Inst_{14}\gets X_{1}=X_{1}+T_{1}\)
35 endif
36
37 endfor
38
39 endfor
40\(x_{q}=\frac{X_{1}}{Z_{1}}\),
41\(y_{q}=x_{p}+(\frac{X_{1}}{Z_{1}})[(X_{1}+x_{p}\times Z_{1})(X_{2}+x_{p}\times Z _{2})+(x_{p}^{2}+y_{p})(Z_{1}\times Z_{2})]\times\) \((x_{p}\times Z_{1}\times Z_{2})^{-1}+y_{p}\)
```  
**Algorithm 1** Montgomery PM algorithm [12]
# Remote Sensing Technologies for Monitoring Argane Forest Stands: A Comprehensive Review
## 4 Remote Sensing Applications  
Remote sensing technology involves the use of various tools and techniques for gathering data and information about the Earth's surface and atmosphere remotely. These technologies allow for the observation, measurement, and monitoring of environmental and geographical phenomena without physical contact.