esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Water scarcity poses a threat to food security [1]. Globally, agricultural activities use roughly 70% of the 1% fresh available water suitable for human consumption and ecological use [2, 3]. In the Republic of South Africa (RSA), agricultural practices are the biggest consumer of water, utilising approximately 62% of the available fresh water [4]. There are conflicting reports on when the water demand will exceed the supply in the RSA [5, 6]. [PERSON] [5] reported that the RSA water demand exceeded the supply in 2017; however, [PERSON] [6] forecast that the water demand will exceed the supply in 2025. As such, alternative irrigation water sources are critical to mitigate water scarcity, which represents a predominant challenge for farmers in the country. Globally, common mitigation strategies for irrigation-water scarcity in dry regions include utilising treated wastewater (TWW) [7].  
The usage of TWW as an alternative irrigation source has been proposed as a feasible option for crop productivity and for a sustainable environment [8; 9; 10]. Macronutrients such as nitrogen, phosphorus, and organic matter (OM) are present in TWW; hence, farmers may consider the usage of this water as a cost-efficient way to supplement the soil available nutrient pool [11; 12]. However, there are drawbacks attached to the use of TWW since TWW is contaminated with non-essential heavy metals [13; 14; 15; 16]. As such, the usage of TWW as an irrigation source may increase the soil phytoavailable pool of non-essential heavy metals [17]. Thus, mechanisms such as bioremediation to offset the accumulation of non-essential elements in the soil are crucial to ensure public health security and soil sustainability.  
Bioremediation is a biotic, eco-friendly, and economically viable technique that degrades and transforms toxic substances into an innocuous state [18]. This biotic method has several advantages over abiotic methods due to the low energy consumption, costs, and by-products that are harmless to crops [19]. Microbes are integral components of bioremediation due to their absolute population and vast chemical appetite [19]. Their highly multifaceted metabolic pathways, enable them to survive in unconducive environmental conditions intoxated with non-essential heavy metals. Hence, microbes are used as biological agents for bioremediation [20]. However, graphic conditions and the accessibility of the pollutants to microbes determine the microbial efficacy for heavy-metal removal [21]. Therefore, using microbes that are indigenous and already existing in contaminated soil systems might work better than those introduced from different environments, since indigenous microbes are already better adapted to the prevailing soil conditions. Therefore, the objective of this study was to determine whether indigenous microbes such as _Escherichia coli_, _Staphylococcus aureus_, _Aspergillus flavus_, and _Pseudomonas aeruginosa_, which are known to be tolerant to toxic elements, would reduce the concentrations of the non-essential toxic elements in soil following irrigation with TWW.
## 4 Discussion and Result Analysis
### Storage Requirements  
Maintaining images in a database could demand large amounts of storage. The database used for testing stored the authentication elements of a total of 52 users with an average of 9 images per user. This database was hosted in Back4 App and had a total of 246 MB in file storage.  
The developed prototype, once deployed and downloaded as a mobile app, would require 75.6 MB of storage.
# Ultrasonic Surface Rolling Process: Properties, Characterization, and Applications
## 3 Microstructural Development during USRP  
In recent years, USRP has been receiving widespread attention among scholars owing to the ability to develop a homogenous surface with precise control of the process parameters. Since USRP is a surface SPD method, it is natural to visualize the changes in the microstructure from that angle. The microstructural changes during USRP could be classified under two broad categories: microstructural changes in ferrous-based materials and non-ferrous-based materials. Both types exhibit three distinct layers. The grain refinement is extensive on the top surface, producing an ultrafine/nanocrystalline structure. Below the top layer, a second layer is present, a region of extensive deformation showing many deformation structures. Below that layer is a region that contains unaffected matrix material [40; 48]. The degree of grain refinement, the extent of plastic deformation, the depth of SPD layers, etc. all depend on the initial microstructure, chemical composition, USRP processing parameters and so forth. At the top, fresh, strain-free grains can also form due to dynamic recrystallization (DRX). Microstructure details generally developed during USRP are schematically presented in Figure 3. The grain size increases towards depth direction, thus generating a GNS layer.  
The following sections will provide a salient view of microstructural features developed during the USRP of ferrous-based and non-ferrous-based materials.
## 4 Experimental Results and Analysis  
### Validation of Attention Mechanisms  
In this paper, the residual full-attention convolution module is employed to process the multi-scale sliced feature information extracted from the first half of the model to accomplish the feature optimization and extraction tasks. The selection of attention mechanisms in the FRAC module is compared with the existing more outstanding and widely used non-local attention mechanisms, and the three major categories of attention mechanisms involved in the test are spatial attention (S), channel attention (C), and spatial-channel hybrid attention (C + S), and the parameters and model structure are kept consistent, and the results are shown in the following Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameter** & **Settings** \\ \hline Activation & ‘softmax’ \\ Optimizer & Adamax \\ Loss function & BCE Loss \\ Batch size & 64 \\ Learning rate & 0.0005 \\ Epochs & 60 \\ Metrics & Dice \\ Input images size & \(512\times 512\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Hyperparameter settings for model pre-training.  
Figure 6: (**a**) An image of an abdominal CT slice from the original dataset; (**b**) An image of an abdominal CT slice after preprocessing.  
The MRFA model, without attention mechanism processing, achieves a Dice score of 93.6% in kidney segmentation tasks. Single-channel attention methods and spatial attention methods are not suitable for handling the multi-scale fusion feature part of this model. This may be because the number of channels in the multiscale fusion feature to be processed is 3840. At this point, the main information of the model lies in the spatial position information of the feature map and the semantic features carried by the channel dimension. A singular attention mechanism consolidates feature information from other dimensions, lacking mutual feature responses. DANet, combining spatial and channel-mixed attention, achieved a better performance, with an improvement of approximately 2.7%, but retained only partial benefits. Among them, FLANet performed the best, as it can obtain feature responses from all other channel maps and their corresponding spatial positions for each channel map, addressing the issue of attention deficiency. Based on the above analysis, FLANet is selected as the mechanism for the attention part of the RFAC module in subsequent experiments.
# An Internet of Things Access Control Scheme Based on Permissioned Blockchain and Edge Computing
## 4 System Model
### Smart Contract System  
Smart contract is a virtual account with the contract code and storage space in the blockchain. To create a smart contract, it is necessary to write, compile, and deploy a smart contract [22]. Both the advent and execution of smart contracts depend on blockchain protocols, which can guarantee the execution of smart contracts. Access control policies are then turned into smart contracts and they are installed on the blockchain.  
There are three sorts of smart contracts involved in this scheme--an administration contract (MC) is used to control different contracts in the system, a permission decision contract (PDC) is shown in the Algorithm 1, which is responsible for obtaining attribute information from the PIP and making final access control decisions, and a massive variety of coverage contracts (PCs) are used to put into effect precise attributes based totally on access control policies.  
MC is accountable for the administration of PDC and PCs in the system. The principal ABI are:  
getCheckContract(): The accountability of this ABI is to attain the tackle of the PDC.  
getContract(): The accountability of this ABI is to reap the tackle of PC.  
According to the acquired subattribute and objattribute, PCs judge whether the subject has access to the object, and the result is \"true\" or \"false\".  
```
Input: AAR Output: Policy decision result, Permit or Deny
1: Permit_Result_Set = null
2: Deny_Result_Set = null
3: result = PolicyDecide(AAR)
4:if (result = Permit) then
5: Permit_Result_Set.add()
6:else
7: Deny_Result_Set.add()
8:end
9:if (Permit_Result_Set \(\
eq\) null && Deny_Result_Set == null) then
10: return Permit
11:else
12: return Deny
13:end
```  
**Algorithm 1** Permission decision contract
## Appendix A  
In the absence of polarization mode dispersion (PMD) and polarization-dependent loss (PDL), the fiber propagation of the complex envelope of a DP optical signal is described by the following equations, as given by [16]:  
\[\frac{\partial A_{x}}{\partial z}-j\frac{\beta_{2}}{2}\frac{\partial^{2}A_{x} }{\partial t^{2}}+\frac{\alpha}{2}A_{x}=-j\gamma^{\prime}\Big{(}\big{|}A_{x} \big{|}^{2}+\big{|}A_{y}\big{|}^{2}\Big{)}A_{x} \tag{11}\]  
\[\frac{\partial A_{y}}{\partial z}-j\frac{\beta_{2}}{2}\frac{\partial^{2}A_{y} }{\partial t^{2}}+\frac{\alpha}{2}A_{y}=-j\gamma^{\prime}\Big{(}\big{|}A_{x} \big{|}^{2}+\big{|}A_{y}\big{|}^{2}\Big{)}A_{y} \tag{12}\]  
where \(A_{x}\) and \(A_{y}\) denote the signal components in the two polarization states, while the sum \((\,\mid A_{x}\,\mid^{2}+\mid A_{y}\,\mid^{2})\) is the total signal power. The variables \(t\) and \(z\) denote the time and distance axes, respectively, \(\alpha\) is the attenuation coefficient, \(\beta_{2}\) is the group velocity dispersion parameter, \(\gamma^{\prime}=(8/9)\gamma\) is the effective nonlinear coefficient, and \(\gamma\) is the nonlinear coefficient [28]. According to [16], the solution of (11) and (12) can be expended into Volterra series transfer function (VSTF) kernels up to the 3 rd-order as follows:  
\[\begin{array}{ll}A_{x}(\omega,z)&=H_{1}(\omega,z)A_{x}(\omega)+\int\int \limits_{-\infty}^{\infty}H_{3}(\omega_{1},\omega_{2},\omega-\omega_{1}+ \omega_{2},z)\\ &\times\Big{[}A_{x}(\omega_{1})A_{x}^{*}(\omega_{2})+A_{y}(\omega_{1})A_{  
\[K_{3}(\omega_{1},\omega_{2},\omega-\omega_{1}+\omega_{2})\approx\frac{j\gamma^{ \prime}}{4\pi^{2}}\times\frac{1-e^{-\alpha L}}{\alpha}K_{1}(\omega)\sum_{k=1}^{ N}e^{jk\beta_{2}L\Delta\Omega} \tag{10}\]  
where \(\Delta\Omega=(\omega_{1}-\omega)\times(\omega_{1}-\omega_{2})\) is the spacing between the discrete frequencies in the sampled spectrum, \(L\) denotes the span length, and \(N\) is the total number of fiber spans. The last two equations (i.e., (10) and (10)) can be realized by the scheme shown in Figure 1a,b, respectively.  
The operating principle of each nonlinear branch is divided into three stages and each polarization tributary passes through them (as described in Figure 1b). The first stage is the CD compensation through a filter with transfer function [16]  
\[\left(K(\omega)\right)^{k}=e^{\frac{i\omega^{2}\beta_{2}kl}{2}} \tag{11}\]  
where \(k=1\),..., \(N\) indicates the number of the fiber span and \(K(\omega)=e^{i\omega^{2}\beta_{2}kl}\) compensates the dispersion of each span. The second stage is the nonlinear compensation realized through the filter \(K_{3,k}(\omega_{1},\omega_{2},\omega-\omega_{1}+\omega_{2})\) with transfer function [16]  
\[K_{3,k}(\omega_{1},\omega_{2},\omega-\omega_{1}+\omega_{2})\approx\frac{j \gamma^{\prime}}{4\pi^{2}}\times\frac{1-e^{-\alpha L}}{\alpha}K_{1}(\omega)e ^{i\omega^{2}\beta_{2}L\Delta\Omega} \tag{12}\]  
Finally, the third stage is used for the compensation of the residual dispersion through the filter \(K(\omega)^{N-k}\) with transfer function [16]  
\[\left(K(\omega)\right)^{N-k}=e^{\frac{i\omega^{2}\beta_{2}(N-k)L}{2}} \tag{13}\]
## 2 Related Work  
### Deep Learning-Based Methods  
Semantic segmentation methods based on deep learning are developing rapidly [16; 17; 18; 19; 20], and these methods are also applicable to the task of segmenting clouds in satellite images. Researchers have added different functional modules to the original network structure to improve the segmentation effect.  
[PERSON] et al. proposed CloudSegNet [21] for efficient cloud segmentation. As a lightweight deep learning architecture, it is the first general framework that deals with daytime and nighttime sky/cloud images in a single framework. The framework achieves great performance with a precision of 0.92, a recall value of 0.87, an F-score of 0.89, and an error rate of 0.08 on the composite dataset of SWIMSEG (Singapore Whole sky IMaging SEGmentation Dataset) and SWINSEG (Singapore Whole sky Nighttime IMaging SEGmentation Database).  
[PERSON] et al. proposed a modified U-Net-based method [22] for the semantic segmentation of clouds and cloud shadows in single-date images from various multispectral satellites without the need for retraining or human intervention. Based on comparative experiments on the globally distributed multi-sensor test dataset, this method consistently outperforms the state-of-the-art methods.  
[PERSON] et al. proposed Cloud-Attu [12] for cloud detection, which is a deep learning model based on U-Net. The Cloud-Attu model introduces an attention mechanism and adopts a symmetric encoder-decoder structure. Using skip-connection operation, the model is able to fuse high-level features with low-level features, which enriches the multi-scale information in the output results. The symmetric structure shows its great advantage in improving the segmentation of images. Based on comparative experimental results, the proposed model outperforms the traditional method in terms of accuracy, even with disturbances from bright non-cloud objects.  
[PERSON] et al. proposed a global attention feature fusion residual network [23] that aims to address the limitations of spectral bands and complex backgrounds in cloud segmentation. The proposed method can automatically classify clouds, shadows, and backgrounds. The model adopts a residual network [24] as the backbone for semantic information extraction and introduces an improved atrous spatial pyramid pooling method for handling boundary information. It also uses the global attention up-sample mechanism for information fusion and improves the utilization of global and local features. Through experiments, the model demonstrates great performance in cloud and shadow segmentation.  
To address the limits of CNN-based cloud detection methods, [PERSON] et al. proposed DCNet [25], a deformable convolutional cloud detection network. The proposed model is more adaptable to cloud variations. Deformable convolution blocks are introduced at the encoder and a skip connection mechanism is integrated into the decoder. The proposed method outperforms state-of-the-art models on GF-1 wide-field-of-view satellite images.
## 5 Conclusions  
This study aimed to investigate the characteristics of warm mix asphalt incorporating coarse steel slag aggregate. The findings of the study illustrate that asphalt mixes incorporating EAF steel slag exhibited better characteristics than the asphalt mixes made with conventional mixes. The stiffness modulus of the produced warm mix asphalt at 150 \({}^{\circ}\)C was the best performing, as compared to the other production temperatures. The warm mix asphalt produced at 30 \({}^{\circ}\)C lower than the reference mix (HMA) exhibited a comparable stiffness modulus to the stiffness modulus of the reference mix incorporated steel slag. Also, the warm mix asphalt produced at a mixing temperature of 10 \({}^{\circ}\)C lower than the hot mix asphalt exhibited superior permanent deformation resistance than the other mixes. Moreover, decreasing the production temperature up to 30 \({}^{\circ}\)C less than the control mix resulted in a similar permanent deformation resistance to that of the control asphalt mix (HMA). The results of the moisture susceptibility experiment demonstrated that the resistance of the produced warm mix asphalt at 130 \({}^{\circ}\)C to moisture damage was slightly lower than that of the reference mixes, while the moisture damage resistance of the produced warm mix asphalt at 150 \({}^{\circ}\)C was the best. Furthermore, the produced warm mix asphalt at 150, 140, and 130 \({}^{\circ}\)C revealed higher stiffness modulus and permanent deformation resistance in comparison to the HMA incorporated natural aggregates. In general, as the mixing temperatures increased, the performance of the warm mix asphalt improved. However, producing the warm mix asphalt mixtures incorporating EAF coarse steel slag aggregate at a temperature of 130 \({}^{\circ}\)C is recommended since the results of the performance tests showed performance comparable to that of hot mix asphalt.  
Conceptualization, A.M.A.; methodology, A.M.A. and M.Y.A.; software, A.M.A.; validation, A.M.A., M.Y.A., H.Y.B.K., and M.R.I.; formal analysis, A.M.A.; investigation, A.M.A.; resources, A.M.A.; data curation, A.M.A., M.Y.A., H.Y.B.K., and M.R.I.; writing--original draft preparation, A.M.A., M.Y.A., H.Y.B.K., and M.R.I.; writing--review and editing, A.M.A., M.Y.A., H.Y.B.K., and M.R.I.; visualization, A.M.A.; supervision, M.Y.A.; funding acquisition, H.Y.B.K. All authors have read and agreed to the published version of the manuscript.  
This research is supported by Universiti Tenaga Nisal Malaysia (BOLD 2020 RJ010517844/046).  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: The presented data are available in the article.  
Figure 15: Plot of the interaction between the factors and the response of the linear model.  
**Acknowledgments: The authors would like to express their gratitude for the provided support by Universiti Tun Hussien Onn Malaysia and Universiti Tenaga National.**
## 3 Discussion
Very few findings have been published on the effectiveness of CCI in combination with anti-NGF therapy for treating chronic pain in rats. Consequently, processes involved in pain relief have not been identified [7]. The present study used behavioural analysis, NGF, and substance P in DRG to show how effective anti-NGF is in reducing chronic neuropathic pain. The substance P that is released in the dorsal horn of the spinal cord is known to participate in the process of modulating pain and transmitting pain signals. There is a correlation between increased secretion and the presence of physical discomfort and acute stress [40]. The release of substance P occurs in response to inflammation in the periphery and noxious stimuli. Substance P plays a crucial role in nociception at the spinal level. Substance P can be secreted in the dorsal horn in response to ordinarily ineffective benign stimuli in rats with behavioural hyperalgesia caused by experimental polyarthritis. After an inflammatory response, the superficial dorsal horn of the spinal cord has elevated levels of NK1 mRNA expression [41]. In the current investigation, we observed the same categories of effects caused by substance P.  
Furthermore, anti-NGF treatment inhibited the upregulation of NGF in DRG, as well as substance P in DRG in response to neuropathic pain. It is hypothesized that the analgesic impact of anti-NGF in chronic neuropathic pain is due to its direct influence on peripheral sensitization by lowering NGF and substance P and its indirect effect on central sensitivity by reducing the activation of the anterior cingulate cortex. This is because anti-NGF has both a direct and an indirect effect on peripheral sensitization. The anterior cingulate cortex may be responsible for the reported brain effect by relaying projections to the spinal cord without affecting synaptic transmission to the grey periaqueductal cortex. This is a possible mechanism that could have caused the effect. In conclusion, caraway oil alleviates the symptoms of neuropathic pain in a CCI model. This could be due to the fact that caraway oil can inhibit the production of pro-inflammatory mediators that are triggered during nerve injury.
# Rapid Estimation of Moisture Content in Unpeeled Potato Tubers Using Hyperspectral Imaging
## 4 Conclusions  
This paper investigated the feasibility of HSI to quantify the moisture content of unpeeled potatoes before they were subsequently stored and processed. Hyperspectral images were collected from 47 intact potato tubers, with partial least squares regression (PLSR) models developed to predict moisture content from these spectra. The models showed predictive abilities for moisture content, with acceptable ratios of prediction to deviation (RPDs) when considering the complete wavelength range (R\({}^{2}\) = 0.53; RPD = 1.46, root mean square error (RMSE) = 5.04%) or the \(\beta\)-coefficient wavelength selection technique (R\({}^{2}\) = 0.53; RPD = 1.47, RMSE = 5.02%). Furthermore, the prediction ability increased by more than 10% when the model wavelength was narrowed down to 733-970 nm. This study demonstrates the potential of using HSI for the quality assessment of intact, unpeeled potatoes, although further work is required to improve the model quality and implement this approach using remote sensing imagery. There are several limitations of this study. This study utilized a very limited sample for modeling and we will include more samples for measuring the model performance in the future. We also intend to test different sizes and varieties of potatoes for predicting moisture content and make an effort to incorporate samples that are collected directly from cultivars. An extended study will be carried out to analyze the spatial spectrum while predicting the moisture content.  
For Conceptualization, P.M., N.H.S., J.J., N.I., S.W. and S.G.; methodology, P.M., N.H.S., J.J. and N.I.; software, P.M., N.H.S., J.J. and N.I.; validation, P.M., N.H.S., J.J., N.I., S.W. and S.G.; writing--original draft preparation, P.M., N.H.S., J.J. and N.I.; writing--review and editing, P.M., N.H.S., J.J., N.I., S.W. and S.G.; supervision, N.H.S., N.I., S.W. and S.G.; project administration, N.H.S., N.I., S.W. and S.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The MC and HSI data are available upon request from authors.  
The authors declare no conflict of interest.