esa-sceva/satcom-chunk-collection huggingface dataset:
# Effects and Mechanisms of Microbial Remediation of Heavy Metals in Soil: A Critical Review
## 1 Introduction  
Soil heavy metal pollution mainly refers to the deposition of heavy metals, such as mercury, cadmium, lead, chromium, and other bio-toxic significant heavy elements in the soil [1], resulting in concentrations that exceed background values. Metals are not biodegradable, and through biological amplification, their concentrations can be increased thousands of times, with significant effects on human health [2]. In recent years, discharges of large volumes of heavy metals from industrial activity and mining, with final deposition in the soil, have led to increases in soil heavy metal concentrations. Widespread use of pesticides and fertilizers may also have led to an increase in soil heavy metal concentrations [3].  
The management of heavy metal pollution in soil relies on two processes: (1) Traditional chemical and chemical restoration methods based on curing and leaching and (2) ecological restoration methods based on adsorption and transfer [4]. Traditional chemical methods usually involve direct reactions between chemical reagents and heavy metal ions, without any other promotion method, such as chelation and redox, while chemical restoration methods are often promoted by other methods, such as electrochemical repair [5]. However, according to [PERSON] [6], traditional methods are often expensive, complicated, frequently cause secondary pollution, and significantly alter the soil structure, among other limitations and deficiencies. Recently, ecological restoration has become more widely used because of its lower cost and measurable ecological, social, and economic benefits. Traditional ecological restoration is generally phytoremediation, i.e., the use of hyperaccumulators to absorb heavy metals from contaminated soils [7]. [PERSON] et al. [8] found that, for two plant species,_Plragmities australis_ and _Ailanthus altissima_, total removal of Cr\({}^{3+}\) from water ranged from 55 to 61%. However, the use of microbial remediation has become more common, and it is generally considered promising owing to its many advantages [9], including retention of soil structure, and the fact that the pollutants and microbes can be almost completely removed from the soils, and secondary pollution can be avoided [10]. Microbial remediation presents new techniques for addressing the problem of heavy metal pollution in soil, and it has become a focus of new research and development in bioremediation technology. This paper reviews the sources and hazards of heavy metals in soil, and discusses the techniques and influencing factors for microbial remediation, providing a useful reference for the restoration of soil ecosystem health.
## 4 Sensitivity Analyses
### Deformation of Surrounding Rock Mass  
#### 4.2.1 Plastic Zones  
It is of great significance to study the changes in the plastic zone range of the surrounding rock to analyze the stability of the bolt support to the surrounding rock. The evolution of the plastic extent with time under different in situ stress conditions was examined, as shown in Figure 11. The range of the plastic zone is denoted by \(R\). It is expressed by a dimensionless symbol K, as  
\[\text{K}=\frac{R}{D} \tag{37}\]  
It is interesting to find that the increase in the plastic zone is not correlated with the rock bolt length, as Figure 11a,b refers to \(\xi\), = 0.3 and \(\xi\), = 0.9, respectively, and are they virtually identical. It can be seen from the figure that, as a whole, as the in situ stress increases, the plastic zone gradually increases. Due to the rheological effect of the development of the rock mass, the maximum value can reach 18 m and grows nonlinearly. In addition, as tunnel excavation time increases, the extent of the plastic zone gradually increases. The numerical simulation results show that the increase in the plastic zone is related to the magnitude of the in situ stress and the excavation time. This finding is consistent with the change in the nonlinear rheological properties of rock mass. It is worth noting that plastic softening in the rock mass is lower than its peak strength until it reaches the residual strength, but it still has a sufficient bearing capacity. Combining Figures 5 and 7, it can be seen that the threshold of the bolt is 9 m, the maximum value of the plastic zone is 24 m, and the bolt does not need to pass through the plastic zone.
# Evaluation of Radio Communication Links of 4G Systems
## 2 Background
### Model of the Propagation  
The propagation model makes it possible to estimate the value of the path attenuation. There are several types of models: an empirical model is a mathematical formula used to predict the impact of a transmitter on a specific reception area, andphysical models predict the radio waves propagation and calculate the paths of radio waves taking into account reflection and diffraction phenomena. For our study, we will choose the empirical propagation model. Its formula depends on several factors, namely: the frequency of the wave; the heights of transmitter and receiver antennas, and the distance.
# Performance Analysis for COVID-19 Diagnosis Using Custom and State-of-the-Art Deep Learning Models
## 5 Discussion
However, it is less than the accuracy achieved in a literature research work by a maximum of approximately 4.4% using another model. Overall, it can be concluded that the accuracy achieved in the current work is reduced by approximately 2.2%, when compared for a 3-class classification. Also, the comparison is limited by the number of deep learning models employed, i.e., only three in this study. Recent studies showed that a dataset, when collected from  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Studies**} & \multirow{2}{*}{**Cases Number X-ray Datasets**} & \multirow{2}{*}{**Method Utilized**} & \multirow{2}{*}{**Accuracy (\%) Binary Classification**} & \multirow{2}{*}{\ \begin{tabular}{c} **Accuracy (\%)** \\ **Multi-Class Classification** \\ \end{tabular}  } \\  & & & & & **Classification** \\ \hline \multirow{4}{*}{[31]} & \multirow{4}{*}{250} & \multirow{4}{*}{2753} & \multirow{4}{*}{3520} & VGG16 with & \multirow{4}{*}{N/A} & \multirow{4}{*}{97\%.} \\  & & & AveragePooling2D, Flatten, & & & & \\  & & & Dense, Dropout and a Dense & & & \\  & & & layer using Softmax function & & & \\ \hline \multirow{4}{*}{[32]} & \multirow{4}{*}{180} & \multirow{4}{*}{200} & ResNet18, ResNet50, & \multirow{4}{*}{ \begin{tabular}{c} 92.6\% (Pre-Trained \\ ResNet 50 Model) 91.6\% \\ (Proposed CNN Model, \\ Average accuracy \\ using different \\ SVM kernels) \\ \end{tabular}  } \\  & & & & & & \\ \hline \multirow{4}{*}{[33]} & \multirow{4}{*}{538} & \multirow{4}{*}{468} & Ensemble of three models & \multirow{4}{*}{95.70\%} \\  & & & & & \\ \hline \multirow{4}{*}{[34]} & \multirow{4}{*}{401} & \multirow{4}{*}{401} & Ensemble of two & \multirow{4}{*}{96.15\%} \\  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \hline \multirow{4}{*}{[27]} & \multirow{4}{*}{181} & \multirow{4}{*}{364}  
different sources, can lead to dataset bias [51; 52] where the model learns the source of the images rather than the disease itself. Images from different sources can have different characteristics, such as overall pixel intensities and corner labels, and may present electrodes with cables in the X-ray, bras, etc. The model will tend to learn these characteristics and associate these for the classification of the disease. For future work, the dataset should also be analyzed for bias. Moreover, other state-of-the-art models can be evaluated on the dataset utilized in the current study. Further, from the obtained performance of the custom CNN model, the authors would like to enhance it further to an advanced level where it would lead to a better diagnosing and demarcating of the subject virus cases from other cases.
# LDA-Based Topic Modeling Sentiment Analysis Using Topic/Document/Sentence (TDS) Model
## 3 Proposed Method
### Introduction to LDA  
LDA is a well-known method for topic modeling. First introduced by [PERSON] et al. in [29], LDA shows topics using word probabilities. LDA is an unsupervised generative probabilistic model of a corpus. The main task of LDA is that documents are represented in a random mixture over latent topics, where a topic is characterized by a distribution over words [30]. LDA assumes that every document can be represented as a probabilistic distribution over latent topics, as shown in Figure 2. In this process, the topic distribution in all documents shares a common Dirichlet prior. Each latent topic represented in the LDA model is also represented as a probabilistic distribution over words, and the word distributions of topics share a common Dirichlet prior. Given a corpus \(D\) consisting of \(M\) documents, with document \(d\) having \(N_{d}\) words (\(d\in 1,\dots,M\)), LDA models \(D\) according to the following generative process [31]:  
Figure 1: Workflow of proposed method.  
1. Choose a multinomial distribution \(\varphi_{t}\) for topic t (t \(\in\){1,..., _T_}) from a Dirichlet distribution with parameter \(\beta\). 2. Choose a multinomial distribution \(\theta_{d}\) for document \(d\) (\(d\in\{1,\ldots,\mathcal{M}\}\)) from a Dirichlet distribution with parameter \(\alpha\). 3. For a word \(w_{n}\) (\(n\in\){1,..., \(N_{d}\)}) in document \(d\), 1 selection of a topic \(z_{n}\) from \(\theta_{d}\), 2 selection of a word \(w_{n}\) from \(\varphi_{2n}\).  
Regarding the generative process above, words in documents are only observed variables, while others are latent variables (\(\varphi\) and \(\theta\)) and hyper parameters (\(\alpha\) and \(\beta\)). To infer the latent variable and hyper parameters, the probability of the observed data \(D\) is computed and maximized as follows:  
\[p\ (D|\ \alpha,\ \beta)=\prod_{d=1}^{M}\int\ p(\theta_{d}|a)(\prod_{n=1}^{N_{d}} \sum_{z_{dn}}p(z_{dn}|\theta_{d})p(w_{dn}|z_{dn},\beta))d\theta_{d} \tag{1}\]  
The \(\alpha\) parameters of the topic Dirichlet prior and the distribution of words over topics, which are drawn from the Dirichlet distribution, are given by \(\beta\). T is the number of topics, M is the number of documents, and N is the size of the vocabulary. The Dirichlet multinomial pair for corpus-level topic distributions (\(\alpha\), \(\beta\)) was considered. The Dirichlet multinomial pair for topic-word distributions is given by \(\beta\) and \(\varphi\). Variables \(\theta_{d}\) are the document-level variables. \(z_{dn}\) and \(w_{dn}\) are word-level variables that are sampled for each word in each text document.  
There have been many studies related to topic models using LDA in different fields, such as topic modeling in linguistic science, political science, medical and biomedical fields, and other research areas [32; 33; 34].
# Research on Unsupervised Classification Algorithm Based on SSVEP
## 3 Results and Analysis
### The Effect of Data Length  
To evaluate the effect of extraction time on SSVEP performance, we process a comparison experiment in terms of the average accuracy rate \(A_{ave}\), average information transmission rate \(ITR_{ave}\) and minimum correct number of subjects \(S_{min}\) conducted on acquisition time intervals of 1.5, 2, 2.5, 3 and 3.5 s. \(S_{1}\) and \(S_{2}\) indicate the numbers of subjects whose accuracy rates are more than 80% and less than 50%, respectively.  
As shown in Table 2, the accuracy can reach the maximum with the increase of time, which is 88.9%. For an SSVEP system with 40 targets and a frequency difference of 0.2 Hz, the recognition rate of nearly 90% can already meet the requirements of various control systems with lower real-time, and more sampling extraction time can be used to ensure the accuracy rate. Moreover, increasing the time extraction length can improve the abnormal samples to some extent, which is shown as the minimum correct number of subjects in Table 2.  
Since the EEG intensity of each subject is different, the acquisition environment and the interference level of the acquisition in some application scenarios are also different, and it is necessary to ensure the minimum number of correct or minimum accuracy. Theoretically, for the time length, the longer, the better, which is confirmed by \(S_{1}\) and \(S_{2}\) in Table 2. However, if the time is too long, after the response time of the stimulus, the acquired data  
Figure 9: The accuracy and \(ITR\) of FBCCA and CCA.  
will revert to the baseline of the EEG signal after the stimulus-response period, which may interfere with the results.
## 4 Design of DDNB
### DDCP (Dynamic Decentralized Certification Protocol)  
SN provides pre-defined services to TN, and TN generates specific transactions to use those services. Before those transactions can access data in BN, DDNB verifies the integrity of the transaction through DDCP (Dynamic Decentralized Certification Protocol). In DDCP, TN segments a transaction logic into several query stages and sends a first query to a randomly selected SN. The selected SN executes one of the divided processes sequentially as shown in Figure 2. During this process, BN generates a short hashed string, _nonce_. DDCP uses this _nonce_ to act as an \"one-time authentication key\" for the transaction. This _nonce_ is removed from the blockchain if the transaction uses the _nonce_ (one-time use), or after a period of time (timeout). From BN's perspective, it is one of the effective ways to verify transactions. When the transaction reaches BN as a final step, BN will not refuse the execution as long as it is a completed form signed by TN. For this reason, the transaction packet can be copied and sent over and over even if it has already been sent and there is no such _nonce_ value. Through the above process, it is possible to determine whether the packet is forged or not, and also the integrity of the transaction can be ensured. In addition, this process can be executed in parallel since multiple SNs with guaranteed integrity would work on concurrently.  
Figure 1: Three-layered Hierarchical Architecture of Doubly Decentralized Network Blockchain (DDNB).
# Ultrafast Optical Heating Induced Polarization-Dependent Optical Switching in Gold Nanowires
###### Abstract  
Excitation using femtosecond laser pulses induced ultrafast heating of discontinuous gold nanowires, resulting in transient thermal expansion of the gold nanostructures that constitute the nanowires. The cross-plasmon resulting from the closely arranged gold nanostructures along the nanowires was modified by the change in the small gaps due to the thermal effect. This led to the spectral shift of the cross-plasmon resonance and laid the photophysical basis for the optical switching. A femtosecond pump-probe scheme was used to investigate the ultrafast optical switching dynamics. The most efficient optical switching effect was observed when the pump and probe laser pulses were polarized perpendicular and parallel to the discontinuous gold nanowires, respectively.  
ultrafast optical heating; optical switch; gold nanowires; plasmonic hot electrons; thermal expansion +
Footnote †: journal: Applied Physics Letters B
###### Abstract  
This study investigated the effects of a dual-purpose inoculant (DPI) on the fermentation profile, nutritive value, and aerobic stability of silage. The inoculant effect was first examined with misisilos, and the results were later validated with 400-kg silo bales and a 40-t bunker silo. Briefly, whole-plant corn harvested at the one-half to two-thirds milk line stage was chopped and then treated with or without inoculant containing _Lactobacillus plantarum_ LP1028 and _Lactobacillus buchneri_ LBC1029 at application rates of 2.5 \(\times\) 105 cfu and 5.0 \(\times\) 105 cfu per gram of fresh forage, respectively. The results showed that applying DPI had no effect on the nutritive value in all trials. DPI inoculation also slowed yeast and mold growth in silage under aerobic exposure. Inoculation may double the aerobic stability time after 105 d of ensiling (53.25 vs. 113.20 h) in a bunker silo. This study successfully examined the effectiveness of DPI in misisilos, and the results were consistent when moving from the laboratory to the field. Applying DPI made the fermentation more heterolactic without compromising the silage nutritive value, and increasing acetic acid acted as an antifungal agent to inhibit spoilage microbial growth and improve silage aerobic stability.  
a _Article_  
Effects of a Dual-Purpose Inoculant on the Quality and Aerobic Stability of Corn Silage at the Laboratory and Field Scales  
1 Culture Collection & Research Institute, Synbio Tech Inc., Kaohsiung 82151, Taiwan;  
1 [EMAIL_ADDRESS] (L-C.K.); [EMAIL_ADDRESS] (T-Y.L.); [EMAIL_ADDRESS] (H-T.C.); [EMAIL_ADDRESS] (S-L.Y.); [EMAIL_ADDRESS] (J-S.L.)  
Hengchun Branch, COA-IRI, Pinguting 946444, Taiwan; [EMAIL_ADDRESS] (S-M.W.); [EMAIL_ADDRESS] (C-S.C.)  
aerobic stability; _Lactobacillus plantarum_; _Lactobacillus buchneri_
## 4 Data Fusion Algorithm
#### Kalman Filter Design  
A Kalman filter consists of 6 steps,  
1. State predicting, \[\begin{bmatrix}\Phi_{int}(k+1,k)\\ \Phi_{ext}(k+1,k)\\ \Phi_{cyt}(k+1,k)\end{bmatrix}=\mathbf{A}(\rho(k))\begin{bmatrix}\Phi_{int}(k, k)\\ \Phi_{exh}(k,k)\\ \Phi_{cyt}(k,k)\end{bmatrix}+\mathbf{B}(\rho(k))\] (19)
2. State covariance predicting, \[\mathbf{S}(k+1,k)=\mathbf{A}(\rho(k))\mathbf{S}(k,k)\mathbf{A}(\rho(k))^{T}+ \mathbf{Q}\] (20)
3. Covariance innovation, \[\mathbf{N}(k+1)=\mathbf{R}+\mathbf{H}\mathbf{S}(k+1,k)\mathbf{H}^{T}\] (21)
4. Filter gain, \[\mathbf{G}(k+1)=\mathbf{S}(k+1,k)\mathbf{H}^{T}\mathbf{N}(k+1)^{-1}\] (22)
5. State estimation updating, \[\begin{bmatrix}\Phi_{int}(k+1,k+1)\\ \Phi_{exh}(k+1,k+1)\\ \Phi_{cyt}(k+1,k+1)\end{bmatrix}=\\ \begin{bmatrix}\Phi_{int}(k+1,k)\\ \Phi_{exh}(k+1,k)\\ \Phi_{cyt}(k+1,k)\end{bmatrix}+\mathbf{G}(k+1)\begin{pmatrix}\mathbf{Y}(k+1)- \mathbf{H}\begin{bmatrix}\Phi_{int}(k+1,k)\\ \Phi_{ext}(k+1,k)\\ \Phi_{cyt}(k+1,k)\end{bmatrix}\end{bmatrix}\] (23)
6. State covariance updating, \[\mathbf{S}(k+1,k+1)=\mathbf{A}(\rho(k))\mathbf{S}(k+1,k)\mathbf{A}(\rho(k))^{ T}+\mathbf{Q}\] (24)
## 6 Computational Experiments
### Sensitivity Analysis
#### 6.3.2 Convergence Behavior  
In order to illustrate the convergence behavior of the two meta-heuristics, multiple tests are performed with different values of the maximum number of iterations for each neighborhood structure (_Max_VNS_) which goes from 1 to 500 iterations. The obtained results are presented in Figure 6 which shows the impact of the value of the number of iterations (_Max_VNS_) on the total average deviation for the second objective (\(F_{2}\)).
# Graphene Deposition on Glass Fibers by Triboelectrification
## 3 Results and Discussion
All rGO coated glass fibers were subjected to surface electrical resistivity measurements to evaluate the extent of the reduction of GO to rGO, and electrical resistivity values are reported in Figure 4.0 As expected, the presence of the conductive rGO layer on the fibers imparted electrical conductivity to the insulating GF, thus demonstrating the validity of the deposition method. However, this is true only for the fibers coated with an elevated GO concentration, i.e., 0.05 and 0.1 rGO coated glass fibers. In the case of 0.005 rGO coated glass fibers, the resistivity measurement was not possible. An explanation could be that, for this sample, the percolation limit, i.e., the formation of a continuous network of reduced graphene oxide on the fiber surface, was not reached, and therefore electrical conductivity is too low to be measured. For 0.05 and 0.1 rGO coated GF, the electrical resistivity showed lower values in the case of 0.1 rGO coated glass fibers. Having such fibers resistivity values much lower than glass, the successful reduction of GO to rGO is confirmed. However, the TE process does not significantly affect the resistivity values of rGO coated fibers. This suggests that the TE process for graphene coating does not significantly increase the amount of GO coating, rather than it only improves the quality of the coatings produced. Figure 4.1 Comparison of interfacial shear strength (IFSS) values obtained from micro debonding tests on uncoated and rGO coated GF.
# A Dynamic Edge Server Placement Scheme Using the Improved Snake Optimization Algorithm
## 3 System Modeling and Problem Description
### Problem Description  
This study aims to determine the optimal edge-server number and placement according to conditional variations, where the edge-server number and placement are constrained by a certain desired latency range and are determined by factors such as the network model, edge coverage, and user latency. The problem can be modeled as follows  
\[\min C \tag{13}\]  
_s.t._  
\[\lambda<\sum_{k=1}^{Z}\delta_{k} \tag{14}\]  
\[\eta\leq\mu \tag{15}\]  
\[T_{\min}\leq T\leq T_{\max} \tag{16}\]  
Constraint (14) indicates that the task data processed at the edge server is less than the sum of the data for all users, Constraint (15) indicates that the user data received by the edge server requires processing that is within the computational capacity of the edge server, and Constraint (16) indicates that the computed average user latency satisfies the defined range.
## 2 Optical Uplink Channel Model
### Statistical Properties of Channel Fading Factors
#### 2.1.3 Link Attenuation  
In deep space optical uplink, two essential factors cause the optical power attenuation, geometric diffusion and atmosphere attenuation. Due to the optical receiving aperture size being limited and the optical divergence angle being nonzero, the light spot at the receiver is much larger than the receiving aperture. Consequently, most of the optical power is lost. This is the geometric diffusion attenuation, which is denoted as \(\zeta_{g}=\left(\pi L_{t}\text{tan}^{2}\theta_{d}\right)^{-1}\), where \(\theta_{d}\) is the optical divergence angle. Moreover, the atmosphere contains numerous molecules and aerosol particles, which cause absorption and scattering of optical power. Specific attenuation effects vary with weather conditions, such as clear weather, foggy, hazy, rainy, and snowy. Attenuation in the atmosphere is represented by the Beers-Lambert law, which is given by \(\zeta_{a}=\exp(-L_{a}\xi)\)[46]. Here, \(L_{a}\) stands for the length of the atmospheric link and \(\xi\) is the atmospheric attenuation coefficient related to weather conditions. In addition, interplanetary dust is not a critical impairment for the scenarios analyzed in this work. Consequently, the link attenuation coefficient \(h_{c}\) is derived as  
\[h_{c}=\zeta_{g}\zeta_{a}=\frac{1}{\pi L_{t}\text{tan}^{2}\theta_{d}\exp(L_{t} \xi)}. \tag{9}\]
###### Abstract  
This article presents the implementation and the evaluation of a transmission-reception system for Physical Downlink Control Channel (PDCCH) within the context of fifth-generation cellular networks (5G/NR). The research focuses on characterizing the behavior of the PDCCH channel concerning different User Equipment (UE) positions through simulations conducted in MATLAB. Two scenarios are considered: a lossless system and a system with losses, where the outcomes are presented in terms of Bit Error Rate (BER) vs. Signal to Noise Ratio (SNR) and BER vs. Transmit Power, respectively. One contribution of the paper is the explanation transmission-reception system for PDCCH in MATLAB, while the second contribution is the presentation of simulation results. From results, it was deduced that higher UE height corresponds to improved channel performance, greater UE elevation angle leads to enhanced channel performance, and increased carrier frequency results in reduced performance of the physical control PDCCH channel.  
5G; NR; PDCCH; UE position; BER; SNR; transmit power +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).