esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Cd Exposure Schemes
#### 3.2.3 Cd Exposure of Dams during Pregnancy-Lactation Period
\begin{table}
\begin{tabular}{p{108.4 pt} p{108.4 pt} p{108.4 pt} p{108.4 pt}} \hline
**Strain (\(n\))** & & & \\
**Dose/Concentration, Admin Route Exposure Period** & **Collection Time Organ Analyzed** & **Results** & **Year Author** \\
**Litter (\(n\)), Lactation Days** & & & \\ \hline  & & Changes in vertical exploration activity and increased exploration of an open field center. Spontaneous and evoked electrophysiological variables showed dose- and generation-dependent variations, signaling a change in neural functions. & 1997 \\  & & At birth, Cd serum levels were increased in exposed group. Serum estradiol of female offspring was decreased at PND 49. Histopathological results showed a sparse arrangement of cells in hippocampal area. Prolonged space latency and exploring time were shown at PND 35 and 49. Learning and memory were affected, especially in female offspring, through changed structure in the hippocampal area and protein expression of \(\gamma\)-aminobutyric acid receptor subtype subunits. & 2013 \\  & & Transferrin receptor was upregulated in the neonatal brain & [PERSON] et al. [44] \\  & & Male offspring at PND 90 presented no differences in testosterone levels, cell proliferation and apoptosis indexes compared with control group, but stromal inflammatory foci and multifocal instances of treated group. & 2016 \\  & & Cdc exposure affected sperm quality (morphology and motility) and increased apoptosis in testis. & 2012 \\  & & Cdc showed neurochemical disturbances on serotoninergic and aminoiodergic systems during development. Hippocampal levels of serotonin and 5-hydroxyindoleacetic acid were significantly reduced but dopamine content was maintained. Glutamate concentration decreased in hypothalamus and increased in hippocampus, while \(\gamma\)-aminobutritic acid decreased only in cerebral cortex & 2010 \\  & & & \\ \hline \end{tabular}
\end{table} 
Table 3: _Cont._
\begin{table}
\begin{tabular}{p{108.4 pt} p{108.4 pt} p{108.4 pt} p{108.4 pt}} \hline
**Strain (\(n\))** & & & \\
**Dose/Concentration, Admin Route** & **Collection Time** & **Results** & **Year** \\
**Exposure Period** & **Organ Analyzed** & **Author** \\  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Strain (_n_)** & **Cllection Time** & **Results** & **Year Author** \\
**Exposure Period** & **Organ Analyzed** & **CORO1A and cofilin 1 were** & 2022 \\  & **PND 21, 35, and 56** & **up-regulated, while profilin 1 was down-regulated in the testis of maternal Cd-exposed male offspring. In the ovarian granulosa cells of female offspring exposed to Cd the lipid droplets were smaller than normal; ADRP was down-regulated, accompanied by decrease in PLC\(\updelta\)2 and PKC\(\upalpha\). The HSL phosphorylation was increased and StAR and CYP11A1 were up-regulated. This series of events resulted in a high level of progesterone in serum. ** & 2020 \\  & **PND 21, 35, and 56** & **and PKC\(\updelta\)
## 4 Materials and Methodology  
The experiments in this study utilized images from the proprietary PYGSA (Valencia, Spain) company database, which were categorized into various classes representing different types of chicken pieces, such as 'yellow wing (C1)', 'white wing (C2)', 'yellow thigh (C3)', and 'white thigh (C4)'. The images, which correspond to trays of chicken with multiple units already placed, were captured using a camera specifically positioned to focus on the designated area of each chicken piece. In total, 878 samples were used for this study. Specifically, there were 206 samples for 'yellow wing (C1)', 251 samples for 'white wing (C2)', 218 samples for 'yellow thigh (C3)', and 203 samples for 'white thigh (C4)'.
## 1 Introduction  
In many signal processing applications [1; 2; 3; 4], we find various sparse channels in which most of the impulse responses are close to zero and only some of them are large. In recent years, many kinds of sparse adaptive filtering algorithms have been proposed for sparse system estimation, including recursive least squares (RLS)-based [5; 6; 7; 8; 9] and least mean square (LMS)-based algorithms [10; 11; 12; 13; 14]. It is generally known that RLS-based algorithms have faster convergence and less error after convergence than LMS-based algorithms [15]. However, there are fewer RLS-based than LMS-based algorithms. Among these, the convex regularized recursive least squares (CR-RLS) proposed by [PERSON] [6] is a full recursive convex regularized RLS like a typical RLS.  
While the aforementioned algorithms typically show good performance in a Gaussian noise environment, their performance deteriorates in a nonGaussian noise environment such as an impulsive noise environment. Recently, the maximum correntropy criterion (MCC) [16; 17; 18; 19] has been successfully applied to various adaptive algorithms robust to impulsive noise. Current studies in robust sparse adaptive methods have resulted in the development of CR-RLS-based algorithms with MCC [20; 21], and showed strong robustness under impulsive noise. However, CR-RLS used in [20; 21] is not practical when determining the regularization coefficient for the sparse regularization term because CR-RLS [6] needs information about the true channel when calculating the regularization coefficients. In addition, MCC CR-RLS algorithms (so called convex regularized recursive maximum correntropy (CR-RMC)) [20; 21] include the inversion of the auto-covariance matrix, which is linked to the numerical instability in finite-precision environments [15].  
The recursive inverse (RI) algorithm [22; 23] and the iterative Wiener filter (IWF) algorithm [24] have recently been proposed. RI and IWF have the same structure besides a step size calculation. Theyperform similarly to the conventional RLS algorithm in terms of convergence and mean squared error, without using the inverse of the auto-covariance matrix. Therefore, RI [22; 23] and IWF [24] can be considered algorithms without the numerical instability of RLS.  
This paper proposes a sparse channel estimation algorithm robust to impulse noise using IWF and maximum correntropy criterion with \(l_{1}\)-norm regularization. The proposed algorithm includes a new regularization coefficient calculation method for \(l_{1}\)-norm regularization that does not require information about true channels. In addition, the proposed algorithm has numerical stability because it does not include inverse matrix calculation.  
In Section 2 of this paper, we derive the new algorithm using IWF. In Section 3, we provide simulation results that show the performance of the proposed algorithm. In Section 4, we note our conclusions.
## 5 Conclusions  
The uncertainties in construction waste generation and the demand for recycled materials are two of the key factors in the planning of recycling centers. To deal with these uncertainties, this paper designs a two-stage stochastic model aiming at maximizing the recycling rate of construction waste and conducting a sensitivity analysis to further reveal the correlation between recycling rate and investment. The proposed model is compared with the mean value decision model through a case study in Guangzhou. The contributions of this paper can be encapsulated into two key domains: (1) This study addresses the limitations of traditional plans for constructing recycling centers, which often fail to determine the optimal location and capacity under conditions of uncertainty. Our comparison of the proposed model with the mean value model, using a real-world case study from Guangzhou, demonstrates that our model attains a commendatory recycling rate. (2) From the perspective of decision makers aiming to maximize recycling rates, this study conducts a sensitivity analysis to evaluate how the construction waste management budget influences recycling rates. The analysis reveals that transportation costs significantlyinfluence the planning of recycling center development, underscoring the need for strategic budget allocation to enhance recycling efficiency.  
All in all, this paper proposes several potential enhancements for the current recycling centers. Principally, diagram theory regarding the optimal plan for facility locations, e.g., Voronoi diagrams [58; 59], can be compared with the two-stage stochastic model. The differences between diagram theory and mathematical methods in the optimal location plan for construction waste management warrant further research. Furthermore, integrating active building information modeling (BIM) into waste management strategies could provide a comprehensive perspective. Active BIM, incorporating optimization methods for quantitative mathematical analysis and the Internet of Things, has been employed to enhance the efficiency of working facilities and construction plans [60; 61]. The recycling process involves multiple stages and the storage of construction waste that cannot be processed immediately. Active BIM optimizes the placement and processing sequences of construction waste, enhancing overall efficiency.  
Conceptualization, H.W. and W.Y.; Methodology, H.W. and W.Y.; Software, S.X. and H.W.; Validation, S.X., H.W. and M.F.A.-A.; Formal Analysis, S.X.; Investigation, S.X. and Y.T.L.; Writing--Original Draft Preparation, S.X. and H.W.; Writing--Review and Editing, Y.T.L., W.Y. and M.F.A.-A.; Supervision, W.Y. All authors have read and agreed to the published version of the manuscript.  
This research is supported by the National Natural Science Foundation of China (Grant Nos. 72201229 and 72361137006) and the Research Grants Council of the Hong Kong Special Administrative Region, China (Project Number 25211722).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
## 2 Materials and Methods
### Multi-Scale Spatial Decoder  
In the task of farmland change detection, accurately modeling and integrating multi-scale information within feature extractors are crucial. To address the issue of potential image information loss during data interpolation, we designed a multi-scale decoder mirroring method, which combines a Transformer decoder and the \(CBAM_{ASPP}\) module. This approach effectively captures and integrates multi-scale contextual information, enhancing the model's feature representation capabilities and generalization performance. Both the Transformer decoder and the \(CBAM_{ASPP}\) module possess unique advantages. The Transformer decoder excels at capturing global contextual information, allowing it to model long-distance dependencies between features, thereby enhancing the completeness of feature representation on a global scale. Specifically, the Transformer decoder employs a multi-head self-attention mechanism and a feed-forward neural network, leveraging positional embeddings to retain spatial information and performing decoding based on the following equation:\[D=\text{TransformerDecoder}(F^{\prime},M) \tag{8}\]  
where F\({}^{\prime}\) represents the input features, and M denotes the features output by the encoder. In contrast, the \(CBAM\_ASPP\) module excels in capturing local features and integrating multi-scale features. By employing parallel multi-scale convolutional branches, the \(CBAM\_ASPP\) module effectively extracts feature information at various receptive fields. Additionally, the channel attention mechanism further enhances the feature representation capabilities, ensuring that important features receive adequate attention. The specific calculation process is as follows:  
\[\text{Branch}_{i}=\text{Conv}_{3\times 3}(D,\text{dilation}=r_{i}) \tag{9}\]  
\[\text{GlobalFeature}=\text{Conv}_{1\times 1}(\text{GlobalAvgPool}(D)) \tag{10}\]  
\[\text{FeatureCat}=\text{concat}(\text{Branch}_{1},\text{Branch}_{2},\text{ Branch}_{3},\text{Branch}_{4},\text{GlobalFeature}) \tag{11}\]  
\[\text{OutputFeature}=\text{Conv}_{1\times 1}(\text{CBAMLayer (FeatureCat)}) \tag{12}\]  
Finally, the output features from the Transformer decoder are summed with the output features from the \(CBAM_ASPP\) module to form the final decoding result.  
This multi-scale decoder mirroring method performs exceptionally well in practical applications, enabling a more accurate detection of farmland changes and enhancing the model's robustness and reliability. The combination of the Transformer decoder and the \(CBAM\_ASPP\) module allows the model to excel in both global information capture and local feature extraction, effectively addressing the information loss issues introduced by data interpolation. Consequently, this enhances the model's feature representation capabilities and generalization performance.
## 2 Materials and Methods
### Experimental Methods  
#### 2.2.1 Batch Biosorption Method  
The biosorption experiments were developed after the contact of an established amounts of new prepared biosorbent biosorbent 1--by micro-encapsulation, and biosorbent 2--by a simple dropping technique, in sodium alginate of the residual biomass of _Saccharomyces pastorianus_ (5% dry matter (d.w.), after the traces of calcium chloride were removed by washing with distilled water) with 25 mL of dye solution with different initial concentrations (in the range 10.88-174.08 mg/L for biosorbent 1, and 10.64-170.24 mg/L for biosorbent 2). The pH values were adjusted with 0.1N HCl solution at the value of pH = 3 (measured with waterproof Combo pH/EC/TDS Testers, Hanna Instruments Inc.) for the BRed dye biosorption (highest adsorption capacities were preliminarily obtained at pH 3) [10; 11]. The working temperature (kept constant) was that of the room environment, around 25 \({}^{\circ}\)C, and the contact time of the phases was varied by no more than 10 h. The remaining dye concentration in the aqueous solution was determined using the spectrophotometer-based method at the maximum dye wavelength of 530 nm, using the  
Figure 1: Chemical structure of reactive Brilliant Red HE-3B dye—C.I. 25810.  
MeterTech SP-830 Plus spectrophotometer (MeterTech Inc., version 1.06). The values of dye removal (\(R\), or \(Y\), (%)), or biosorption efficiency (\(q\), (mg/g)) were calculated with the Equations (1a) and (1b):  
\[R\ (or\ Y)=\frac{C0-Ct}{C0}\cdot 100 \tag{1a}\] \[q=\frac{C0-C}{G}\cdot V \tag{1b}\]  
where \(C_{0}\) and \(C_{t}\)--the initial and remaining dye (at \(t\) biosorption time) concentration in the aqueous solution (mg/L), \(G\)--the biosorbent amount (g) and \(V\)--the aqueous solution volume (L).
# Experiment and Analytical Model for Pore Structure of Early-Age Composite Cement Pastes by LF-NMR
###### Abstract  
This study investigated mineral admixtures that are often utilized as replacements for cement in high-performance concrete with a view to enhancing their durability and workability. The properties of concrete are closely related to the structure of its pores. This research employed low-field nuclear magnetic resonance technology to explore the influence of water-to-cement ratio, curing time, and mineral admixture content on the pore structure of early-age cement pastes. The findings indicated that the pore size distribution curves of all composite cement pastes display a distinct bimodal nature. The size of gel pores increases with a higher water-to-cement ratio, but decreases as the curing period extends. Fly ash, slag, and silica fume improve the pore structure at 14 days, 7 days, and 3 days, respectively. The addition of admixtures has little effect on the most probable pore diameter, but raises the proportion of gel pores with increasing content. In order to better fit the experimental data, a bimodal model integrating [PERSON] and [PERSON]'s model with the Weibull distribution function was introduced to describe the pore structure of cement pastes with or without fly ash, slag, and silica fume.  
composite cement paste; early age; pore structure; LF-NMR; bimodal model Article
# Reflection Characteristics Measurements of Indoor Wireless Link in D-Band
## 5 Results and Discussions
### Relative Permittivity  
Through the analysis of the monotonicity between the \(|\Gamma_{//}|\) and \(\epsilon_{r}\) in (7), it can be concluded that the Fresnel reflection coefficients will improve as the relative permittivity increases. The rank of the broken lines with different relative permittivity in Figure 6a-e also verifies this conclusion. In addition, by comparing the estimated relative permittivity with the published data in Table 7, it can be concluded that the estimated dielectric constants of materials in the D-band are still relatively accurate with a slight deviation. It can be also found that the broken lines in Figure 6a-e are basically distributed around the black solid lines, although the test frequencies of relative permittivity corresponding to the black lines in Figure 6d,e do not reach the D-band range [24; 42]. This is attributed to the weak frequency dependence of the dielectric constant. The small deviation between the estimated relative permittivity and the previously published results is caused by the following reasons [27]. First, there are differences in the materials, i.e., the exact composition and surface roughness. Second, no measurement can eliminate the error completely, which will lead to discrepancies. Last but not least, different processing methods of the dataset and different experimental environments also play a role. Overall, the differences are acceptable, and our results are comparable to those in previous references. This indicates that the measurement campaigns, the established model, and dataset processing proposed in this paper are reliable and robust.  
The previous research suggests that the relative permittivity of the samples does not change significantly with the transmission frequency, even over a wide frequency range. It is also found that the dielectric constant is usually related to the variation of sample compositions rather than the frequency [43; 44]. Figure 8 demonstrates the estimated relative permittivity of five kinds of materials versus the frequency. It is easy to conclude that the relative permittivity decreases with slight fluctuations from 110 GHz to 170 GHz for a certain material, but the change is quite subtle. For example, the relative permittivity of wood decreases from 1.92 to 1.75 with increasing frequency in D-band and the largest gap is only 0.17, which is less than one-tenth of the relative permittivity. In the same way, the largest gaps are just 0.13, 0.18, 0.32, and 0.27 respectively, corresponding to plexiglass, drywall (plaster), concrete board, and red brick. In addition, the relative permittivity of different materials changes significantly at a certain frequency. The values of concrete board and wood are the largest and smallest, respectively. The results in Figure 8 are consistent with the previous research and further confirm the reliability of our work.  
Figure 8: The relationship between estimated relative permittivity of five materials and frequencies in D-band.
# Machine Learning Algorithm for Delay Prediction in IoT and Tactile Internet
## 7 Conclusions  
This paper proposes ML methods for delay prediction in IoT and tactile internet networks, using the k-step prediction approach with the NARX-enabled RNN technique.  
ANN was trained using four different algorithms: Trainbr, Traincgf, Trainlm, and Trainrp, considering the MSE loss function as a performance measure to investigate which prediction model provides optimal accuracy and maximum average improvement. The prediction accuracy was measured in terms of RMSE and MAPE as a measure of prediction accuracy.  
The results show that the model predicted by the Trainbr training algorithm outperforms its competitors and has the best prediction accuracy for both 1-step prediction and 15-step prediction. Moreover, the model trained with the algorithm Traingr outperforms its competitors for the case of 10-step prediction. On the other hand, the model predicted by the algorithm Trainrp has poor prediction accuracy compared to the others.  
For future work, the authors suggest the following research plans.  
* The development of algorithms that can consider all the dynamic parameters of the IoT environment and more accurately predict upcoming traffic.
* The development of deep learning algorithms to predict and study the performance based on LSTM, Bi-LSTM, GRU, stacked autoencoder (SAE), and simple recurrent unit (SRU) using loss functions cross-entropy, MSE, MAE, and SSE.
* The development of deep learning based on robust loss functions using robust statistical estimators, such as Cauchy, Huber, and Fair, in the presence of outliers (anomalies).
* The development of deep-reinforced learning for network prediction and security.  
**Author Contributions:** Conceptualization, A.R.A. and A.K.; Data curation, A.R.A.; Formal analysis, A.R.A.; Funding acquisition, R.K.; Investigation, A.R.A., O.A.M., R.K., A.P. and A.K.; Methodology, A.R.A. and A.K.; Project administration, A.K.; Resources, A.R.A., O.A.M., A.P. and A.K.; Software, A.R.A.; Validation, A.R.A., O.A.M. and A.K.; Visualization, A.R.A., O.A.M., R.K., A.P. and A.K.; Writing--original draft, A.R.A.; Writing--review & editing, A.R.A., A.P. and A.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The publication was prepared with the support of the grant of the President of the Russian Federation for the state support of young Russian scientists--Doctor of Science MD--2454.2020.9.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** This research is based on the Applied Scientific Research under the SPbSUT state assignment 2021. The researcher [PERSON] was funded by a scholarship (Ph.D.) under the Joint Executive Program between the Arab Republic of Egypt and the Russian Federation.  
**Conflicts of Interest:** The authors declare that there are no conflicts of interest.
## 1 Introduction
[37] studied the seismic response of uncertain transmission lines by combining LHS and IDA methods. The results showed that the influence of structural parameter uncertainty should be considered when assessing the seismic performance of transmission tower-line system. [PERSON] et al. [38] considered the probability distribution of ice and wind speed, and introduced the uncertainty of structural parameters to analyze the failure probability of American pine power poles under combined ice and wind loads. However, in this study, ice load and wind load were assumed to be independent variables, and the correlation between the two environmental factors was not considered. In addition, the probability of the wind direction and the fluctuating effect of wind were ignored. A comprehensive review of the above-mentioned works indicates that there is currently limited research on the failure probability of transmission lines under combined ice and wind loads, considering the uncertainties of structural parameters and the fluctuating effect of the wind. Therefore, to accurately evaluate the failure probability of transmission lines under combined ice and wind loads, based on [PERSON] et al. 's studies [29; 39], this paper established the uncertain transmission tower-line system models using the LHS method, and then considering the fluctuating effect of wind, a fragility analysis of transmission lines under combined ice and wind loads was conducted. Finally, the failure probability of the tower-line system was calculated based on joint probability distribution models of ice thickness-wind speed (IT-WS) and wind speed-wind direction (WS-WD). In Sections 2 and 3, the failure probability analysis and the uncertain FEMs of transmission tower-line systems are introduced, respectively. In Section 4, the limit performance index for the tower-line system is determined. In Section 5, the failure probability of the transmission tower-line system under combined ice and wind loads is calculated and the influences of the uncertain parameters and fluctuating wind are discussed. Section 6 concludes the study.