esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Genotoxicity Evaluation  
In vitro micronucleus test (MNvit) was performed following OECD (Organization for Economic Cooperation and Development) 487 guidelines (OECD, 487) [14]. The study was carried out using human peripheral lymphocytes from fresh blood samples. Peripheral blood was taken from a middle-aged, non-smoking healthy male who volunteered for this study, signed an informed consent and had not been recently exposed to genotoxic agents (chemical substances, ionizing radiations, bacterial/viral infections). Approximately, 10 mL of blood was collected using heparin-based anticoagulant. About 0.5 mL of heparinized blood was transferred in a tube containing 4.5 mL PB-MAX (GIBCO, Thermo Fisher Scientific, Waltham, MA, USA) Karyotyping medium supplemented with phytohemagglutinin, bovine serum albumin (BSA), amino acids, and antibiotics, all purchased from Merck (KGaA, Darmstadt, Germany). The lymphocytes were incubated at 37 \({}^{\circ}\)C in an atmosphere containing 5% CO\({}_{2}\) for 48 h. Tested materials were fragmented into small pieces and inoculated in the human lymphocytes culture 48 h after the culture was initiated. They were previously sterilized being exposed to UV for 15 min.  
In our previous studies [10,15,16] we found that samples with high concentrations of compounds or nanomaterials in lymphocyte culture cannot be used for genotoxicity assessment because they exceed the maximum allowed concentrations for a correct assessment without false positive results. There is also agglutination and precipitation of the culture. For this purpose, three concentrations of each compound, PDMS, PDMS-BP, and PDMS-BP-TiO\({}_{2}\), were tested: 2 mg/L, 4 mg/L and 8 mg/L. A sample (free of compounds and TiO\({}_{2}\) nanoparticles) was used as negative control. Each sample was tested in duplicate. The cultures were sacrificed 24 h after compounds were added, a period of time which is considered sufficient for the cells to undergo cell division, so that potential DNA damage in the form of micronuclei in interphase cells could be identified. The culture was developed without adding cytochalasin B (CytoB is a chemical agent used to block cytokinesis because it inhibits actin assembly). In the next steps potassium chloride solution 0.075M was used for hypotonization for 45 min at 37 \({}^{\circ}\)C, then cells were fixed in 3:1 methanol/acetic acid solution. Microscope slides were prepared, stained using Giemsa solution. Digital images were obtained using an Olympus BX40 fluorescence microscope (Olympus Life Science, Waltham, MA, USA) and processed with QuickPhoto Micro 2.3 software (PROMICRA, s.r.o., Prague, Czech Republic). Micronuclei were scored in 1000 cells per culture, two cultures per concentrations.
## 4 DASH Live Broadcast Traffic Model
### Maximum Delay Analysis  
In the transmission structure proposed in this paper, the maximum total delay \(\Delta_{O}\) can be expressed as the sum of the maximum queuing delay generated in the token bucket queue and the maximum buffering delay generated in the leaky bucket as follows in Equation (9):  
\[\Delta_{O}=\text{Max}(\delta_{i})\leq T_{q}+\frac{LB}{LC}+\sigma,\ (i=1,2,3,\ldots,\ N) \tag{9}\]  
where \(\delta_{i}\) represents the delay, and \(\sigma\) represents the propagation delay, packet processing delay, and so on. This time is very short. Therefore, this paper assumes that \(\sigma=0\) for simplicity.  
It is critical to predict the maximum delay because the viewers can receive service smoothly after the maximum delay when accessing the broadcast service. Therefore, the maximum delay \(D_{init}(0)\) that the users can experience when they access the live broadcast scheme is as follows:  
\[D_{init}(0)=\Delta_{O}+\rho=T_{q}+\frac{LB}{LC}+\rho. \tag{10}\]
## 2 Digitalization and Cybernation of Work Processes in Manufacturing Systems
### Description of Manufacturing Systems  
In reviewing the literature, we found that the terms \"production\" and \"manufacturing\" have been used interchangeably.  
The International Institution for Production Engineering Research (CIRP) has defined manufacturing as _\"... a series of interrelated activities and operations involving the design, material selection, planning, production, quality assurance, management and marketing of the products of the manufacturing industries.\"_[30].  
According to CIRP, production is _\"... the act or process of actually physically making a product from its material constituents, as distinct from designing the product, planning and controlling its production, assuring its quality\"_[30].  
The term \"manufacturing system\" was first introduced by [PERSON] in his paper [31] in the 1960s. [PERSON] introduced the systems approach to manufacturing philosophy as an important conceptual tool for modeling and controlling objects in manufacturing at the macro level.  
In [24; 32] [PERSON] defined a factory as a complex adaptive manufacturing system (CAMS) and introduced three levels of organization: (1) the corporate (decision-making) level, (2) the management level, and (3) the manufacturing level.  
At the corporate level, business policy is set in terms of the type of products, the volume and the product mix, management, financing, location, profit targets, and so on. The management level comprises a set of agents and/or multiple agents, such as operations management, which are responsible for implementing the policy set by the board at the corporate level. The manufacturing level consists of a number of multiple agents required to transform an innovation or prototype into a marketable product.  
In [24; 32] [PERSON] defined the elementary work system (EWS). An EWS is a basic building block of manufacturing systems at the manufacturing level and consists of the work process, the process implementation device (PID) and the Subject, in our case a human, see Figure 8.  
Due to the increasing uncertainty in markets and the intense communication between elements of manufacturing systems, their behavior is becoming more complex and unpredictable, making them more difficult to manage. The elements of modern manufacturing systems are not only real, physical and social elements, but, there are also many digital and virtual or cyber elements. Such systems represent the generation of advanced manufacturing systems [25].  
In an advanced manufacturing system, three systems are combined: a physical system, a cyber system, and a social system. Therefore, the emerging cyber-physical-based manufacturing systems are a logical and practical step in the evolution of manufacturing systems.
## 3 Results
Figure 2: Box plots for mean voice-discrimination thresholds in quiet and noisy conditions for children (\(n=16\)) and adults (\(n=24\)), separately for the F0, formants, and combined (F0 + formants) cues. Box limits encompass the data between the 25 th and 75 th percentiles, with the median marked by a continuous line within the box. Bars extend to the 10 th and 90 th percentiles, while outliers are indicated by black dots. The mean is depicted as a dashed line within the box. * = \(p<0.05\) between the children and adults, *** = \(p<0.001\) between the quiet and noisy conditions. Additional RM ANOVAs separately conducted for each age group revealed significant acoustic cue X condition interactions (for the adults: F(2,23) = 27.702 \(p<\) 0.001, \(\eta^{2}\) = 0.546; for the children: F(2,15) = 4.420 \(p\) = 0.027, \(\eta^{2}\) = 0.228). Post hoc analyses of the interactions showed that in quiet conditions, better VD thresholds were achieved with the formant cues compared with the F0 cues. However, in noisy conditions, for adults, better thresholds were achieved with F0 compared to formant cues, whereas for children, similar thresholds were achieved with both cues. Yet, the best VD thresholds were achieved with the combined cues in both quiet and noisy environments. Pearson coefficient correlations were conducted for the VDs with each voice cue between the quiet and noisy conditions, separately for the children and adults. The results revealed significant medium-to-strong positive correlations between VD thresholds in quiet and noisy conditions for both groups across voice cues (Figure 3). It is important to note that the correlation based on F0 cues improved (R = 0.801 \(p<\) 0.001) when one outlier was excluded (a child who achieved good VD in quiet conditions but extremely poor VD in noisy conditions). * = \(p<\) 0.05, *** = \(p<\) 0.001.
## 4 Cryptographic Algorithms
### Present-80  
PRESENT [36] is an ultra-lightweight block cipher, developed as a collaboration between Ruhr-University, Germany, and the Technical University of Denmark back in 2007. The algorithm is also based on the SPN structure, the same as PRINCE, and consists of 31 rounds. The input size is 64 bits and the supported key lengths by PRESENT are 80 bits and 128 bits. In the original paper [36], the 80-bit version was recommended over the 128-bit version, which was more than adequate and suitable for low-security applications such as small sensor systems, hence the name PRESENT-80.  
Each round of PRESENT-80 consists of an XOR operation with the round key \(K_{i}\) (for \(1\leq i\leq 32\)), a substitution process with a non-linear layer of S-box, and a linear bitwise permutation process using the pLayer. For the XOR operation, the round keys are generated from the 80-bit supplied key, which was designed to be stored in the key register, denoted as \(K=k_{79}k_{78}\ldots\ k_{0}\). The round key \(K_{i}\) at the round \(i\) is the 64 leftmost bits of the \(K=k_{79}k_{78}\)\(\ldots\ k_{0}\). After the extraction of the round key, the \(K=k_{79}k_{78}\ldots\ k_{0}\) is updated as follows and demonstrated in (3).  
* Rotate the key register \(K\) by 61-bit positions to the left.
* The four leftmost bits are substituted using the S-box.
* The bits of \([k_{19}k_{18}k_{17}k_{16}k_{15}]\) are XORed with the current round number counter (5 bits).  
\[\begin{array}{ll}1.&[k_{79}k_{78}\ldots k_{1}k_{0}]=[k_{18}k_{17}\ldots k_ {20}k_{19}]\\ 2.&[k_{79}k_{78}k_{77}k_{76}]=S[k_{79}k_{78}k_{77}k_{76}]\\ 3.&[k_{19}k_{18}k_{17}k_{16}k_{15}]=[k_{19}k_{18}k_{17}k_{16}k_{15}]\oplus round \_counter\end{array} \tag{3}\]  
PRESENT utilized a 4-bit to 4-bit substitution S-box, with the hexadecimal notation of input \(x\) and output of the function \(S(x)\) given by Figure 3. To fully substitute the 64-bit cipher state, 16 of these S-boxes were utilized in parallel to generate the output.  
The simplicity of PRESENT and intention to be implemented in hardware were cited as the key design goals. PRESENT would demand roughly the same hardware resources for both encryption and decryption. The S-box, the permutation pLayer, and the FSM for round key scheduling are the three primary components that need to be designed in the implementation. The S-box and pLayer can be implemented in hardware as a bit manipulation module, and the S-box can also be utilized again throughout the round key generation process. For the decryption process, the inverted S-box and inverted pLayer also need to be designed.  
Figure 4: The permutation position table of PRESENT.  
Figure 3: The substitution function of PRESENT.  
the accelerator is expected to be low as it only has 16 additions, 16 XORs, and 16 constant distance rotations of 32-bit words.
###### Abstract
Therefore, complex image backgrounds naturally hide the noise [14], resulting in a limited effect in extracting the noise features in complex images. In this paper, we construct a dense residual feature extraction network (DRFENet), which is composed of a Dense Enhancement Block (DEB), a Residual Dilated Block (RDB), a Feature Enhancement Block (FEB), and a Simultaneous Iterative Reconstruction Block (SIRB). We propose an interval transmission strategy based on skip connection layers in DEB, which improves both noise extraction and noise information exchange between shallow convolution layers. It effectively solves the problem of communication between models. At the same time, to make the model obtain more rich fine-grained features, we propose a combination strategy of concatenated dilated convolution [15] and skip connection in RDB. The module systematically connects the outputs of the different scaled receptive fields to separate noise information from the multi-scale background. To eliminate the negative impact of information loss caused by the pooling layer when the receptive field remains unchanged, DRFENet comprehensively uses dilated convolution instead of the pooling layer. In Section 2, we introduce some related works, including the CNN-based model for image denoising, skip connections, and dilated convolution. In Section 3, we explain our CNN-based denoising method in detail. In Section 4, we provide the experimental data and results of the proposed image denoising method. In Section 5, we provide the conclusion and points for future development. The supporting code can be downloaded at: [[https://github.com/zhongruize123/DRFENet](https://github.com/zhongruize123/DRFENet)]([https://github.com/zhongruize123/DRFENet](https://github.com/zhongruize123/DRFENet)).
# Nickel (Ni\({}^{2+}\)) Removal from Water Using Gellan Gum-Sand Mixture as a Filter Material
## 1 Introduction  
Heavy metals dissolved in water can lead to bioaccumulation and might pose a risk to human health when consumed through the food chain [1, 2]. To protect soil and groundwater from adjacent contamination sources (e.g., municipal waste landfills, and agricultural and industrial wastewater), hydraulic barriers have been actively applied in practice. In general, hydraulic barriers use inorganic adsorbents, organic adsorbents, and biosorbents owing to their properties of hydraulic conductivity reduction, high shear strength, and high contaminant removal capacities [3, 4].  
Recently, multiple studies have suggested biosorption as an eco-friendly approach for aqueous contaminant removal [5, 6, 7, 8] and ground hydraulic conductivity control [9, 10]. The principal mechanism of ion adsorption involves the formation of complexes between a metal ion and functional groups (carboxyl, carbonyl, amino, sulfonate, and phosphate), which are present on the surface or inside the porous structure of biological materials [11]. Furthermore, the hydrophilicity of biopolymers that form hydrogen bonds with water molecules [12] results in pore-clogging, mitigating fluid flow, and transport through porous media [13].  
In this study, nickel (Ni\({}^{2+}\)) and gellan gum (GG) were used to investigate the Ni\({}^{2+}\) adsorption and removal capability of GG biopolymer-sand mixtures. GG is known as aneffective material for soil strength enhancement and hydraulic conductivity reduction [9; 14]. Nickel metal is an allergen [15] and a potential immunomodulatory and immunotoxic agent for humans [16]. Furthermore, clinical studies on humans and animals exposed to Ni\({}^{2+}\) concluded that Ni\({}^{2+}\) is carcinogenic to humans [17]. A previous study demonstrated the effectiveness of GG for Ni\({}^{2+}\) removal compared with that of other polymeric materials (Figure 1) [6]. However, Ni\({}^{2+}\) adsorption and removal from an aqueous Ni\({}^{2+}\) solution has not been clearly investigated. Therefore, it is crucial to study the adsorption of Ni\({}^{2+}\) ion by GG biopolymer to assess the feasibility of using a GG-sand mixture as a filter or hydraulic barrier material to ensure sustainable development.
## 3 Results and Discussion
### Chemometric Analysis of VOCs  
PCA is a widely employed technique for analyzing multidimensional data sets in an unsupervised fashion. The PCA result can show the trend of metabolome separation between the groups and indicate the differences in the metabolome within the sample group [36; 37]. To better distinguish and present the differences in VOCs in rice grains across different ripening stages, we conducted a PCA on all samples based on their VOC profiles. The PCA results showed that component 1 (PC1) and component 2 (PC2) explained 41.47% and 14.05% of the variance across the four ripening stages in three rice cultivars, respectively (Figure 1C). PCA revealed a lower variability among the biological replicates. The rice grain of four ripening stages was obviously separated. The separation of all samples along PC1 and PC2 underscores the significant metabolic diversity among the different ripening stages.  
Additionally, a hierarchical clustering analysis (HCA) was performed on all detected VOCs, with the results presented as a heatmap (Figure 1D). The metabolite accumulation patterns of the four periods were different. The VOCs were categorized into four groups: Group I, Group II, Group III, and Group IV. The relative content of VOCs in Group I and Group III gradually increased as the grains matured, while the content in Group II and Group IV progressively decreased. The content of VOCs in rice grains undergoes a dynamic shift during ripening, with certain VOCs increasing or decreasing as the grains mature and others peaking at specific stages. This accumulation pattern caused by grain maturation was further supported by HCA.  
The supervised OPLS-DA model is effective in filtering out orthogonal variables that are irrelevant to the classification variables in metabolites, thereby distinguishing observations between groups and identifying differential metabolites [38]. We employed OPLS-DA to analyze all VOCs of YX430, MXZ2, and YJSM2, respectively (Figure 2A,C,E), and brown rice in 30 d (Figure 2G). The accuracy of the model was validated through 200 permutation tests, resulting in R2Y and Q2 scores exceeding 0.9 (Figure 2B,D,F,H), indicating that the OPLS-DA model did not suffer from overfitting [39]. Each stage of the three varieties can be independently modeled into one group. At maturity, YX430, MX2, and YJSM2 were modeled as one group, respectively.
## 2 Materials and Methods
### Supervised Classification Algorithms
#### 2.3.1 Support Vector Machine (SVM)
The parameters were varied randomly using trial and error to check the algorithm's performance. Different kernels in combination with these cost (penalty) parameters and gamma coefficient were tried. For remote sensing data, the gamma parameter values were always set to the inverse of the number of bands used in the study [28]; therefore, since there were six Sentinel-2 MSI bands, the gamma parameter was set to 1/6.
## 7 Case Study
### Effectiveness of the Proposed Sensor Deployment Scheme
#### 7.3.3 Case with Redundancy  
Since it is easy to run out of energy for a single sensor, it is common to deploy redundancy in each grid. As the sleep/wake-up protocol PEAS is adopted in our model, only one sensor will be active per grid. We compare the lifetime and success transmission rate considering the number of redundant units as \(1,2,3,4,5\), respectively. The simulation results are shown in Figures 11 and 12.  
As shown in Figure 11, the lifetimes of both deployment models increase as the redundancy increases. The lifetime of hexagonal deployment is always longer than that of a random one, and the difference between them grows as more redundant units become available. The data success transmission rate of hexagonal deployment is also higher than that of the random one. Figure 11 implies that when the redundancy rate is higher than 2, the value of \(S\) does not change significantly. However, for the random deployment, it increases significantly as the number jumps from 1 to 2 since the sensor density is too low when the redundancy is 1.  
\begin{table}
\begin{tabular}{c c c} \hline
**Simulation Results** & **Hexagonal Deployment** & **Random Deployment** \\ \hline \(\tau\) & 822.48 h & 804.72 h \\ \(S\) & 0.903943 & 0.300116 \\ \hline \end{tabular}
\end{table}
Table 5: Optimization results of uniform deployment.  
Figure 10: Lifetime under different redundancy rate.  
Therefore, we can conclude that redundancy is useful in improving the lifetime of WSN under the constraint of a required success transmission rate. Moreover, the hexagonal deployment that we proposed is much better than a random one under different redundancy rates.
## 4 Simulation and Experiment Analysis
### Experiment Results  
Based on the linear oscillating load system built above, experiments are finished to analyze the dynamic performance of the LOM with multiple external force loads, i.e., spring load, damping load and rectangular load. Position outputs of the LOM under multiple force loads are depicted in Figures 16-18, respectively.  
As can be seen in Figure 16, the experimental result indicates that the spring force load affects both phase responses and amplitude responses. As the phases advance by 7.2\({}^{\circ}\), 14.4\({}^{\circ}\) and 25.2\({}^{\circ}\), and the amplitude attenuations increase by 7.4%, 14.9% and 19.2%, according to different amplitude spring force loads of 100 N, 200 N and 400 N, respectively. Experimental results and simulation results shown in Tables 2 and 3 have the same effects on the amplitude and the phase lag to be smaller. Although impacts between simulation and experiment are different because of different closed loop controls and differences between physical model and mathematical model, the trend is similar.  
Figure 16: Experimental position outputs under spring forces.  
Figure 17: Experimental position outputs under damping forces.  
As can be seen in Figure 17, damping forces mainly compress the amplitude of the output position as the amplitude attenuations increase by 9.3% and 16.3% accorded with the amplitude damping force loads of 200 N and 400 N, respectively.  
As can be seen in Figure 18, rectangular forces mainly compress the amplitude of the output position as the amplitude attenuations increase by 9.8% and 26% according to the amplitude rectangular force loads of 200 N and 400 N, respectively. Experimental results and simulation results of damping force and rectangular force loads shown in Tables 4 and 5 indicate that damping force and rectangular force mainly have effects on the amplitude attenuation.  
The analyses above are to show dynamic performance of the LOM with multiple types of force loads, but the robustness of the controller used in the closed loop of the LOM is inadequate. If an external force compensation control strategy is applied to the closed loop control of the LOM, the amplitude of the rectangular force can be 800 N, as depicted in Figure 19.  
\begin{table}
\begin{tabular}{c c c} \hline
**Amplitude** & **Simulation** & **Experiment** \\ \hline
200 N & 6.1\% & 14.9\% \\
400 N & 11.5\% & 19.2\% \\ \hline \end{tabular}
\end{table}
Table 4: Amplitude attenuation with damping force load.  
\begin{table}
\begin{tabular}{c c c} \hline
**Amplitude** & **Simulation** & **Experiment** \\ \hline
100 N & 2.7\% & 7.4\% \\
200 N & 4.1\% & 14.9\% \\
400 N & 5.5\% & 19.2\% \\ \hline \end{tabular}
\end{table}
Table 2: Amplitude attenuation with spring force load.  
Figure 18: Experimental position outputs under rectangular forces.  
\begin{table}
\begin{tabular}{c c c} \hline
**Amplitude** & **Simulation** & **Experiment** \\ \hline
200 N & 6.1\% & 14.9\% \\
400 N & 11.5\% & 19.2\% \\ \hline \end{tabular}
\end{table}
Table 5: Amplitude attenuation with rectangular force load.