esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Low-Density Parity-Check Decoder and Its Field-Programmable Fate Array Implementation
## 4 Improved LDPC Decoder Hardware Design and Implementation  
### Control Module and Overall Architecture  
As shown in Figure 4, the overall architecture of the LDPC decoder utilizes the control module to coordinate all other modules. This is achieved in conjunction with the shift address update section, which synchronizes operations across the entire decoder structure. The control module ensures that all modules interact seamlessly, enabling efficient decoding.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Optimal \(\alpha\) Value** & \multicolumn{2}{c}{**Number of Addition and Shift**} & \multicolumn{2}{c}{**Number of Addition and Shift**} & \multicolumn{2}{c}{**Difference in the Number of Addition**} \\
**for NMS** & **Operations Required per Iteration** & **Operations Required per Iteration for** & & **and Shift Operations between the** \\
**Algorithm** & **for NMS with a Given \(\alpha\) Value** & **TNMS with a Given \(\beta=1/a\) Value** & & **Two Algorithms** \\ \hline
0.7 & 6m & 8m & 4n & 4n + (n + 2m)/3 & 6m - 4n & (22m - 13n)/3 \\
0.71 & 6m & 8m & 4n & 4n + (n  
First, once the input buffer module receives the start signal, it begins working, reading the 672 initial channel information values into 24 dual-port RAM blocks, known as llr_ram. After all the signals have been read in, the buffer input module sends a completion signal to the control module. At the same time, the control module sends the initial start signal to llr_ram, which then uses the shift address update section to load the channel information into the H_ram group.  
After receiving the completion signal from llr_ram, the control module sends start signals to the 12 CNP modules and llr_ram. The 12 CNP modules then begin updating the 336 rows of check node information and return them to the H_ram group. Meanwhile, under read-write signal control, llr_ram completes the self-update of its information using correction factor \(\beta\).  
After receiving the completion signals from the CNP modules, the control module sends operation signals to the 24 VNP modules and the decision module. The 24 VNP modules update the 672 columns of variable node information and return them to the H_ram group. Simultaneously, the decision module reads the initial channel information from llr_ram and the check node information from H_ram. After accumulation, it uses the sign bit to output the final decision.  
Once the control module receives the completion signals from the 24 VNP modules and the decision module, it sends a start signal to the verification module. If the verification module's XOR operation yields zero, decoding is successful, and the decoded output is provided. Otherwise, decoding continues to the next iteration.  
Every third iteration, the control signal simultaneously sends a signal to shift the channel information in the llr_ram and the check node information in the CNP module to the right by one position.
## 8 Discussion
### Challenges
#### 8.1.4 A New Physical Layer That includes Subterahertz Bands as Well as VLC  
The use of sub terahertz bands and VLC is required to support very high data speeds (as much as terabits per second) to enable, for example, holographic communications.  
(1) Energy-efficient communication solutions are likely to become increasingly crucial, especially as the Internet of Things (IoT) becomes more widely deployed, with a plethora of tiny sensors. Ref. [388] discusses energy-harvesting mechanisms, sophisticated wireless charging methods, and their fundamental limits, with a focus on potential distributed laser charging approaches that demonstrate wireless charging can transfer up to 2 W of power over a distance of about 10 m. (2) The use of ambient backscattering, which allows tiny devices to run without a battery by redirecting ambient radio-frequency (RF) signals without requiring active RF transmission [389], will be an even more radical approach.
# Optimized Weighted Ensemble Approach for Enhancing Gold Mineralization Prediction
## 3 Results and Discussion
### Comparative Analysis
The mean squared error (MSE) and root mean squared error (RMSE) for this model are 0.17 and 0.44, respectively, lower than the corresponding values for the other models. This finding suggests that the RFKNN \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Metric** & **RF** & **K-NN** & **GPR** & **DT** & **FCN** & **RFKNN-MPA** \\ \hline R & 0.70 & 0.71 & 0.72 & 0.55 & 0.36 & 0.77 \\ R\({}^{2}\) & 0.49 & 0.497 & 0.52 & 0.29 & 0.13 & 0.597 \\ MSE & 0.24 & 0.26 & 0.25 & 0.30 & 0.37 & 0.17 \\ RMSE & 0.49 & 0.51 & 0.50 & 0.57 & 0.64 & 0.44 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Performance predicting gold content using the proposed method and machine learning models on the testing dataset. Figure 8: Scatterplots comparing actual gold content vs. predicted values for RF, KNN, GPR, FCN, DT, and the proposed method RFKNN-MPA for training data. MPA model is more accurate than the other models in predicting the gold content of the testing dataset. The RFKNN-MPA model performed best on the testing dataset for gold content prediction using the proposed technique. These results are encouraging and indicate that the model has excellent generalizability. However, it is required to perform further validation and testing on other datasets to conduct a comprehensive analysis of the model's performance. Figure 9 depicts a radar map that compares the performance of six models, including RF, KNN, GPR, FCN, and DT, and the proposed RFKNN-MPA hybrid model. The study evaluated the accuracies of several models for predicting gold content using four metrics: R, R\({}^{2}\), MSE, and RMSE. The radar plot displays different lines for training and testing data for each model, with testing data exhibiting better performance than training data. The results show that the RFKNN-MPA hybrid model outperformed the other models in all four metrics for training and testing data. As a result, the hybrid model is the most accurate and reliable model for estimating gold content in the study region. In this comparative study of models, the RFKNN-MPA model outperformed the other models (including both machine learning methods and geostatistical approaches) in terms of R, R\({}^{2}\), MSE, and RMSE values, exhibiting higher R and R\({}^{2}\) values than the other models. These results suggest that, compared to the other models, the RFKNN-MPA model was able to explain a more significant proportion of the variance in the dependent variable and produce more accurate predictions. It is essential to bear in mind that the success of machine learning models can be influenced by a wide range of factors, including the quality and quantity of data, the features and hyperparameters used, and the specific application. Therefore, it is essential to conduct a thorough analysis of the performance of various models and select the one best suited for the particular task. Figure 9: Radar plots for (**a**) R, (**b**) R\({}^{2}\), (**c**) MSE, and (**d**) RMSE.
# A Stochastic Bulk Damage Model Based on Mohr-Coulomb Failure Criterion for Dynamic Rock Fracture
## 3 Numerical Results
### Homogeneous Material
#### 3.1.2 The Effect of Load Amplitude  
In the previous example, the stress level was sufficiently high to initiate damage near the loading edges, from the early stages of the solution. The stress state in the middle of top and bottom faces is approximately similar to bi-axial compressive condition; material tends to expand in the horizontal direction because of the Poisson effect while the surrounding material prevents its deformation. However, the stress state around the corners is close to an unconfined uni-axial compressive condition because of the stress-free conditions at left and right boundaries. The higher differences between compressive stresses in the Mohr circle results in a higher value for \(c\); cf. (2). Thus according to the MC failure criterion, the corner zones are more susceptible to an earlier time for damage initiation and higher damage values. This is verified by the higher damage values around the corners in Figure 7a,b. After the initiation of damage at corners, damage diffuses towards the middle of the domain.  
To study the effect of load amplitude, we reduce the peak stress such that damage initiates in the middle of the domain. The vertical normal stress magnitude roughly doubles across the entire width when the stress waves collide in the middle of the domain. The load for this problem is chosen such that it is not large enough to initiate damage when the stress wave enters from the top and bottom edges, but is sufficient to cause damage in the middle of domain due to the doubling effect. We call this condition the low amplitude case, corresponding to \(P_{\text{peak}}=6\) MPa, and refer to the previous peak stress problem as the high amplitude case. As shown in Figure 8a, the initial damage occurs when the peak stress reaches the middle of the domain; i.e., at \(t_{\text{collision}}\approx t_{\text{ramp}}+\frac{l}{2c_{d}}\approx 24\)\(\upmu\)s which is well predicted by the numerical result. After the collision, the magnified reflected waves are sufficiently high to overcome the cohesion of rock. Thereafter, damage diffuses toward boundaries where the waves are propagating to; see Figure 8b-d. This failure mechanism is completely different from that of the high amplitude case where the damage initiates in a shear dominated regime at the corners. Therefore, load amplitude has a significant impact on damage pattern and failure mechanism. For a better comparison, we provide the damage response at various times for the high amplitude case in Figure 9.  
Figure 8: Damage evolution for the medium mesh and low amplitude load at times: (**a**) 24.7 \(\upmu\)s, (**b**) 36.1 \(\upmu\)s, (**c**) 42.2 \(\upmu\)s, and (**d**) 48.2 \(\upmu\)s. The results are shown on the deformed meshes with a magnification factor of 1000.
# A Novel Low-Complexity Fault Diagnosis Algorithm for Energy Internet in Smart Cities
## 3 The Proposed Strategy
### STUKF- and MB-based Centralized Fault Diagnosis
#### 3.1.2 \(Mb\)-based Faulty Cluster-Heads Identification  
Once we obtain \(\hat{x}_{e}(k+1)\), the \(MB\) is applied to find the faulty cluster-heads. First, we assume \(\theta(k)\) obeys the normal distribution when the system is fault-free, which is \(\theta_{i}\sim N(\theta_{i}^{0},\sigma_{\theta_{i}^{0}}^{2})\). If the system suffers from the parameter exception, it can be determined by the following hypothesis test, where \(s_{i}\) is fault-free if \(H_{0}\) holds; otherwise, \(s_{i}\) is faulty.  
\[H_{0}:d_{\theta_{i}}(k)\leq\beta_{\theta_{i}}, \tag{8}\]  
\[H_{1}:d_{\theta_{i}}(k)>\beta_{\theta_{i}}, \tag{9}\]where  
\[\mu_{\theta_{i}}(k)=\frac{1}{N_{1}}\sum_{j=1}^{N_{1}}\hat{\theta}_{i}(k-j|k-j), \tag{10}\]  
\[\sigma_{\theta_{12}}^{2}(k)=\frac{1}{N_{1}-1}\sum_{j=1}^{N_{1}}[\hat{\theta}_{i }(k-j|k-j)-\theta_{i}^{0}]^{2}, \tag{11}\]  
\[\sigma_{\theta_{22}}^{2}(k)=\frac{1}{N_{1}-1}\sum_{j=1}^{N_{1}}[\hat{\theta}_{i }(k-j|k-j)-\mu_{\theta_{i}}(k)]^{2}, \tag{12}\]  
\[d_{\theta_{i}}(k)=\frac{\sigma_{\theta_{12}}^{2}(k)}{\sigma_{\theta_{i}^{0}}^{ 2}}-ln\frac{\sigma_{\theta_{22}}^{2}(k)}{\sigma_{\theta_{i}^{0}}^{2}}-1. \tag{13}\]  
To be more specific, for each \(\hat{\theta}_{i}(k+1|k+1)\), the MB is applied to determine the faulty state of \(s_{i}\). The meter \(s_{i}\) suffers from the parameter bias fault at \(k=\tau\), only if there exists a \(\theta_{i}\) such that \(H_{1}\) holds. For the jump type fault, we need to obtain an accurate fault amplitude as follows. If there exists an integer \(M\in\{0,1,2,\cdots\}\) such that for each \(T\in\{MT_{2}+1,\cdots,MT_{2}+T_{2}\}\), \(MT_{2}\geq\tau\), we have  
\[|\hat{\theta}_{i}(T|T)-\hat{\theta}_{i}(T-1|T-1)|<\varepsilon_{i}, \tag{14}\]  
then, at \(k=MT_{2}+T_{2}\), the fault amplitude of \(s_{i}\) is estimated to be  
\[\Delta\theta_{i}=[\frac{1}{T_{2}}\sum_{T=MT_{2}+1}^{MT_{2}+T_{2}}\hat{\theta}_ {i}(T|T)]-\hat{\theta}_{i}^{0}. \tag{15}\]  
Note that if a smaller \(\varepsilon_{i}\) is chosen, then faults can be more accurately estimated, which requires much more time, and vice-versa.
_Applied_  
_sciences_  
_Article_  
Sensitive Detection of _E. coli_ in Artificial Seawater by  
**Aptamer-Coated Magnetic Beads and Direct PCR**  
**[PERSON] 1,2**, [PERSON] 2\(\copyright\), [PERSON] 1, [PERSON] 1,**  
**[PERSON] 1,* and [PERSON] 1,* \(\copyright\)**  
Received: 6 November 2019; Accepted: 6 December 2019; Published: 10 December 2019  
**Featured Application:**_E. coli_ **in seawater can be simply and efficiently detected by direct PCR. The sensitivity of detection is significantly improved using magnetic beads based bacterial pre-concentration.**  
**Abstract:** Foodborne and waterborne _E. coli_ remains a major economic burden worldwide. Assay seawater for trace levels of _E. coli_ is challenging since it applies time-consuming preparations, expensive instrumentation and complicated procedures. Therefore, there is a continued demand for new analytical technologies that can detect low bacterial concentrations in a more cost- and time-effective manner. In this study, combination of _E. coli_ pre-concentration with a direct polymerase chain reaction (PCR) was shown to enable rapid bacterial detection without enrichment step or DNA extraction/purification. The E1 aptamer that targets _E. coli_ surface epitope grafted onto magnetic beads efficiently concentrated _E. coli_ from water samples containing high concentration of NaCl. When direct PCR was performed on bacteria attached to these aptamer-modified magnetic beads, a limit of \(10^{3}\) CFU/mL was obtained. The overall analysis was performed in less than 3 h. This approach may lead to a future PCR-based biosensor system for online monitoring of enteric bacteria in seawater.  
**Keywords:** seawater; water pathogen; bacterial pre-concentration; direct PCR
## 5 Results for Evaluating the Proposed Method
### Experiment Results
\[\rm E_{i,local}=P_{i,local}*T_{i,local}\] (3) where \[\rm T_{i,local}\ =\ \frac{C_{ci}}{f_{local,j,k}}\] (4) \[\rm P_{i,local}=C_{local,j,k}*V^{2}_{local,j,k}*f_{local,j,k}\] (5)  
A device \(\rm D_{j}\) should evaluate if it is more suitable to execute the job locally or remotely, taking the battery level and latency into account as model restrictions. As the battery level is such an important component in the decision, the system will welcome a strategy that decreases energy usage. Equation (6) expresses the local expense of one task i.  
\[\rm Cost_{i,local}=u_{localT}*T_{i,local,total}+u_{local}*E_{i,local} \tag{6}\]  
The coefficients \(\rm u_{localE}\in[0,1]\) and \(\rm u_{localT}\in[0,1]\) are weightings, where \(\rm u_{localT}+u_{localE}=1\). According to [PERSON], these variables reflect a trade-off between implementation time and energy usage, reducing costs. In the best- and worst-case situations, the DVFS-related overhead rate is between 0.02% and 2% [38]. The suggested technique's energy consumption analysis is shown in Figure 12 compared to the ABC, GA, and PSO algorithms. The obtained results show that energy consumption has significantly decreased compared to the previous methods. Figure 11: Comparison of the results of the load balance degree. * _Delay time_  
The delay time is one of the most significant metrics for assessing the network performance. The suggested technique's delay analysis is shown in Figure 13 compared to the ABC, GA, and PSO algorithms. The obtained results show that the delay time has significantly decreased compared to the previous methods. Figure 12: Comparison of ABC, GA, PSO, and the proposed method in energy consumption. \[\text{LBDY}=[1-(\text{SDY LA})]\times 100\% \tag{2}\]0 Comparison of ABC, GA, PSO, and the proposed method in delay time.
## 3 Modeling of the VLC System and Its Components in the Indoor Environment
### Received Power in the VLC System  
Integrating the reflection and spectral transmission factors into a preliminary received power equation, we obtain the following expression for a flat mirror:  
\[P_{r,ij}^{\text{flat}}=P_{s}\frac{A_{e}}{d_{ij}^{2}}\cos^{m}(\theta_{ij})R( \theta_{ij})T_{ij}(\lambda). \tag{13}\]  
For a convex mirror,  
\[P_{r,ij}^{\text{convex}}=P_{s}\frac{A_{e}}{d_{ij}^{2}}\cos^{m}(\theta_{ij})F_ {\text{conv}}(R_{ij})R(\theta_{ij})T_{ij}(\lambda), \tag{14}\]  
where \(P_{s}\) is the power transmitted by the LED, \(A_{e}\) is the effective area of the photodiode, and \(R(\theta_{ij})\) and \(T_{ij}(\lambda)\) are now more precisely defined to capture the real optical properties of the reflective surfaces.  
Consequently, we can deduce that the total power received, for both scenarios with flat mirrors and convex mirrors, respectively, is obtained by summing the contributions of all mirrors as follows:  
\[P_{r}^{\text{flat}}=\sum_{i=1}^{M}\sum_{j=1}^{N}P_{r,ij}^{\text{flat}}, \tag{15}\]  
\[P_{r}^{\text{convex}}=\sum_{i=1}^{M}\sum_{j=1}^{N}P_{r,ij}^{\text{convex}}. \tag{16}\]  
Now, for a better derivation with more realistic parameters of the received power model of the VLC system, it is important to include the effects of the NLoS channel and the incidence angle at the receiver. These factors play a vital role in signal propagation in an indoor environment, where the signal reaches the receiver through multiple reflections.  
In an NLoS environment, the received power depends not only on the reflection from surfaces but also on how the signal strikes the receiver and the characteristics of the channel, including signal dispersion and attenuation.
## 3 Results and Discussion
### Exergy Efficiency of the AGMD Modules
Hence, the major contributor to the exergy destruction is the heat loss from the modules to the surroundings and the latent heat transferred with the permeate. The heat transfer rates across the module can be assisted by employing condensation plates with good heat conduction, minimizing heat losses to the module frames and covers, and using membranes of lower heat conduction properties. Considering each component of the MD system as open, the exergy destruction contributions can be pinpointed. Doing so will enable system designers to optimize the performance of the components. The result from calculations of exergetic destruction contributions of each component from both MD systems is summarized in Figure 8. The maximum fraction of exergy destruction in both MD types comes from the MD modules. However, the percentage is higher for the Xzero module (58%) than for the Elixir500 (43%). The pumps are in both cases the lowest contributors of exergy destruction. The re-concentrating tank contributed 34% of the exergy destruction to the Elixir500 unit, which was caused by the heat losses through the steel wall and evaporation occurred through openings in the cover as source of heat was electric heaters. Even though in both types of MD modules it is found that the MD modules contribute the highest exergy destruction, the Elixir500 modules showed a lower percentage than those of the Xzero. The lower percentage of exergy destruction for the Elixir500 MD module is mainly due to the improved heat transfer capability of the condensation plates used and hence the higher degree of heat recovery from the cooling side of the module. A literature search for exergy efficiency analyses for related water purification processes, such as RO, revealed wide discrepancies in the values. Table 2 summarizes exergy efficiency for some of the standalone and integrated processes. In contrast to MD, the majority of the exergy rates in RO are derived from the high pressures involved, i.e., 15-25 bars for brackish water and 60-80 bars for seawater, hence the related pressure drop as reflected in improved efficiencies when a pressure exchanger is considered [19]. The very low exergy efficiency of MD reported [4] was from the one powered by solar, and the high exergy losses were due to the MD and heat exchanger units. The MD units' exergy efficiency values are relatively closer to each other than with RO. If we make comparisons  
Figure 8: Percentage contribution of exergy destruction from the different components of each AGMD system. between systems of similar processes and capacities, we see, for example, that the MD with a capacity of 0.3 m\({}^{3}\)/day [4] and Elixir500 from the present study have comparable exergy efficiency.
## 7 Passive Optical Network
### Xgs-PON Application  
The XGS-PON is a novel scheme that proffers a generic solution for the NG-PON system. Furthermore, it can be deemed as a simple alternative to TWDM-PON in which the wavelength tunability is excluded for economic reasons [412]. Moreover, effective coexistence can occur between the XGS-PON and TWDM-PON on the same fiber infrastructure. This is because the utilized wavelengths by each scheme are not the same. As a result of this, the network operators can make use of the lower cost XGS-PON solution for a swift provision of 10 Gbps services. Additionally, this will allow them to take advantage of 10 Gbps service opportunities for instant deployments. Furthermore, when XGS-PON is implemented, there can be a gradual upgrade, cost-effective, and well-structured evolution to the full TWDM-PON system with minimum or no service disruption. Moreover, it can aid the TWDM-PON scheme by facilitating its deployment by means of wavelength by wavelength method. Consequently, the approach will certainly assist in the pay-as-you-grow scheme that is intended for efficient system migration and upgrade [392, 412].  
In addition to its potential for rendering 10 Gbps both in the US and DS directions, XGS-PON also has a significant capability for supporting dual-rate transmission [411]. Consequently, the 10/10G XGS-PON ONUs, as well as 10/2.5G XG-PON ONUs, can be attached to the same OLT port using a native dual US rate TDMA scheme. Note that the offered cost by the XGS-PON dual rate is comparable to that of XG-PON. Nevertheless, it can deliver 4 times the XG-PON US bandwidth. Moreover, XGS-PON has been perceived by some operators as a transitional solution to the NG-PON2. This is owing to its capability for rendering the related NG-PON2 high-data rates along with the XG-PON1 CAPEX efficiency [412, 392, 413, 414]. Moreover, in the DS and UP, GPON utilizes 1490 nm and 1310 nm, respectively. Similarly, in the DS and UP, XGS-PON employs 1578 nm and 1270 nm,respectively. A typical PON-based transport network architecture for RAN is depicted in Figure 24.