esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Microbiological Analyses: 16S Ribosomal RNA Gene Next Generation Sequencing (NGS)  
The microbiological survey was carried out in December 2019 on samples from the multilevel piezometers C5a-c to refine knowledge about the bacterial community in the groundwater. This survey was limited to cluster C5 because it was carried out to investigate the possible influence of microbial activity on some isotopic features, with emphasis on deuterium content in the C5 groundwater.  
Water samples (1 L) were filtered through sterile mixed esters of cellulose filters (S-PakTM membrane filters, 47 mm diameter, 0.22 um pore size, Millipore Corporation, Billerica, MA, USA) within 24 h after collection. Bacterial DNA extraction from the sample filters was performed using the commercial kit FastDNA SPIN Kit for soil and the FastPrep(r)Instrument. Once the DNA extraction was complete, the quantity and integrity of the DNA were evaluated by electrophoresis in 0.8% agarose gel, containing 1 ug/mL of Gel-RedTM, in the running buffer TAE 1X (40 mM tris base, 20 mM acetic acid, and 1 mM EDTA pH 8), and amplification reactions were performed by polymerase chain reaction (PCR). The 16S rDNA profiles of the bacterial communities in the samples were obtained by NGS technologies at the Genprobio SrI Laboratory (Parma, Italy). Partial 16S rRNA gene sequences were obtained from the extracted DNA by PCR, using the primer pair Probio_Uni and/Probio_Rev, which targets the V3 region of the bacterial 16S rRNA gene sequence [48], and the primer pair ArchV46 for archaeal 16S rRNA [49]. Amplifications were carried out using a Verity Thermocycler (Applied Biosystems Foster City, CA, USA), and PCR products were purified by the magnetic purification using Agencourt AMPure XP DNA Purification Beads (Beckman Coulter Genomics GmbH, Bernried, Germany) to remove primer dimers. Amplicon checks were carried out as previously described [48]. Sequencing was performed using an Illumina MiSeq sequencer with MiSeq Reagent Kit v3 chemicals. The fastq files were processed using a custom script based on the QIIME software suite [50]. Quality control retained sequences with lengths between 140 and 400 bp and mean sequence quality scores >20, whereas sequences with homopolymers >7 bp and mismatched primers were omitted. To calculate downstream diversity measures, operational taxonomic units (OTUs) were defined at 100% sequence homology using DADA2 [51]; OTUs not encompassing at least two sequences of the same sample were removed. All reads were classified to the lowest possible taxonomic rank using QIIME2 [50; 52] and a reference dataset from the SILVA database v132 [53]
## 6 Implementation
### Levels of SPD  
The SPD multi-metric evaluates the SPD value for all the demonstrated attributes. ULCL offers a single SPD level for the secure storage of local data of \(ULCL=\langle 80,50,30\rangle\), which affects the confidentiality aspects of a node. WiFi enables the wireless communication of devices (nodes) and also provides a single SPD level of \(WiFi=(20,20,50)\), which affects data integrity property of a network component. IPsec offers authentication and encryption and supports three SPD levels for relevant cryptographic key sizes (\(IPsec_{128}=\langle 68,20,70\rangle\), \(IPsec_{191}=\langle 75,20,70\rangle\), \(IPsec_{256}=\langle 85,20,70\rangle\)). if all node components in a network intercommunicate via IPsec, the SPD factor for data confidentiality is enhanced. The node attribute PEP supports three levels of SPD based on the underlying security configurations (\(PEPE_{plain}=\langle 10,30,10\rangle\), \(PEPE_{Auth}=\langle 50,50,50\rangle\), \(PEPE_{Ent\_Auth}=\langle 80,70,80\rangle\)). In the same manner, PBAC incorporates four SPD states between the communication of the PEP and PDP modules (\(PBAC_{plain}=\langle 10,30,10\rangle\), \(PBAC_{Auth}=\langle 50,50,50\rangle\), \(PBAC_{Enc\_Auth}=\langle 80,70,80\rangle\), \(PBAC_{WS\_Soc}=\langle 90,80,90\rangle\)). CompoSecReasoner's SPD is estimated at \(CompoSecReasoner=\langle 70,50,70\rangle\). All metrics are assessed dynamically at runtime. The security parameter is supported by several protection features, such as IPsec and JADE-S. PBAC safeguards the overall privacy. Dependability is enhanced by all the deployed mechanisms.
## 2 Navigation and Localization
### Localization and Navigation Overview  
General conclusions in terms of sensors performance for non-traditional AUV navigation and localization technologies are shown in Table 4.  
After the literature review, it can be considered that acoustic-based technologies still a reliable alternative for AUV localization and navigation; although they require more infrastructure than others. Future work must consider the possibility to include them in teams of collaborative AUVs. To achieve that, acoustic systems must overcome low updated rates and limited accuracy (at long ranges) in order to avoid collisions in AUV formations, especially when they are navigating in a few meters of each other. On the other hand, visual-based localization technologies--including SLAM--have gained attention in recent years. These technologies can estimate both, position and orientation, contrary to acoustic methods. They also reach a higher accuracy which is critical for the collaborative navigation of AUVs. Thus, it is an interesting and reliable option for some particular tasks under specific environments. Nevertheless, most of them are on an early level of readiness since they have been tested only in very controlled environments. It seems difficult for visual-based systems to overcome the challenging conditions of underwater. Moreover, it is hard for researches to find the proper conditions to test their visual-based and visual-SLAM algorithms in real underwater conditions. To deal with that, some datasets are being collected such as the AQUALOC dataset [97], which is dedicated to the development of SLAM methods for underwater vehicles navigating close to the seabed. The Autonomous Field Robotics Laboratory (AFRB) [98] has some datasets available for the same purpose.  
Figure 14: Sensor fusion alternatives for AUV positioning.
## 3 Results and Discussions
### Flow Distribution at Chamber, Ingate and Die Cavity  
After the liquid metal was poured into the chamber, there was still some air above the liquid surface. In order to build up a wave that could force the air to the front and to prevent the entrapment of air during the injection process, the piston should be moved forward slowly [18]. The simulation results of the flow distribution in the chamber during the slow stage of injection are shown in Figure 6. As the piston velocity increased, the liquid wave also increased gradually. When the piston velocity was set to 0.1 m/s or 0.2 m/s, the liquid metal remained relatively stable. As the piston moved forward and the liquid metal rose steadily, the air was forced to the front and was discharged out of the chamber. When the piston velocity reached 0.4 m/s, the liquid wave became more obvious. When the piston velocity exceeded 0.4 m/s, it had the potential to cause a breaking wave, which would result in air entrapment.  
Figure 4: Simulation results of die surface temperature during preheating cycles: (**a**) selected points on die surface and (**b**) simulation results of temperature curves of selected points.  
Figure 5: Castings produced during the preheating cycles: (**a**) casting obtained during the first cycle, (**b**) casting obtained during the fourth cycle and (**c**) casting obtained during the tenth cycle.  
When the liquid metal reached the ingate, the slow stage of injection switched rapidly to the fast stage of injection for the die cavity filling. In this process, the flow velocity at the ingate increased rapidly, and the flow velocity of liquid metal at the die cavity increased accordingly. The simulation results of the flow distribution at the ingate and the die cavity during the fast stage of injection are shown in Figure 7. When the piston velocity was set to 0.8 m/s, the velocity of liquid metal at the ingate was about 23 m/s. When the liquid metal entered the die cavity, the velocity of liquid metal was small, and the density of fluid vectors was small accordingly. When the piston velocity was up to 3.2 m/s, the flow rate of liquid metal at the ingate was about 90 m/s. Compared with the above conditions, when the liquid metal entered the die cavity, the liquid metal flow rate was larger and the density of fluid vectors was also significantly increased, which would help to improve the filling and feeding capacity of die castings.
## 4 Conclusions  
The proposed methodology for measuring of robotic arm positioning accuracy was verified on its construction consisting of three arms Arms I, II, III, while Arm I was manufactured by two different technologies AdM and CvM. A series of repeated measurements was performed for each robotic arm configuration with two different measurement methods, CoM and N cM. The results of measurements were verified by statistical methods, based on which unsatisfactory values of measurements were excluded from the evaluation. The use of proposed methodology is not only in the field of metrology and testing, but also to verify the interchangeability of components in construction of robotic systems. The methodology provides a detailed view of a dimensional chain quality of the robotic arm structure and determines conditions for maintaining accuracy during disassembly and reassembly of individual components of robotic system structures. Statistical evaluation of the results verified that the data obtained were measured correctly and have a normal distribution. For CoM with the same load of Arm I--AdM and Arm I--CvM:  
* homoscedasticity was confirmed for: CoM_0_Zz, CoM_1_Zz, CoM_2_Xx and CoM_2_Yy;
* the conformity of the mean values has not been confirmed. This means that for interchangeable Arm I it is necessary to modify the design of ArM I--AdM.  
For N cM with the same load of Arm I--AdM and Arm I--CvM:  
* homoscedasticity was confirmed for: NcM_0_Y, NcM_1_Y, NcM_2_Y, NcM_0_Z, NcM_2_Z;
* the conformity of the mean values for NcM_2_X has been confirmed. This means that for mutually interchangeable Arm I--AdM, Arm I--CvM, to increase its rigidity, the structural design of the sensor holder must be modified. Considering the results obtained, the paper follows up the work of [PERSON] and [PERSON] (2019). The authors addressed the accuracy of robotic arm positioning in their work, too. However, they did it from the point of view of its control system. A similar issue is also covered in the work of [PERSON] and [PERSON] (2019). The authors also focus on accuracy of robotic arm positioning. Specifically, they dealt in detail with its payload. Another paper focusing on this issue is by [PERSON] et al. (2019). Even in this case, the problem of robotic arm positioning accuracy was solved in detail. In this case, however, the research was focused on stepper motors that implement the positioning of the robotic arm. It follows from the above examples that the presented research brings a new area of research to the issue of positioning the robotic arm, which needs to be further addressed in order to further expand the knowledge that can be transferred to the application. [PERSON] et al. (2019) is another paper that dealt with positioning accuracy. The authors took a closer look at the calibration procedure, where a simple low-cost procedure improves the accuracy of surface positioning. This is the issue similar to one presented in the article. The difference is mainly that the authors used a two-arm robot, compared to our researched single-arm robotic arm.
## 4 Numerical Simulations and Results
### Results and Discussion
#### 4.2.1 Motion Responses
Figure 8: The mean and standard deviation values of the main motions in the X direction. Figure 11 shows the results of the time history and the PSD values conducted on the sway motion of the CSMI FOWT for case 8 in the Y direction. As shown in Figure 11, the range of sway motion is larger than the other two situations when taking into account the wave-current interaction, and the current tends to amplify the dynamic response of the sway motion in the frequency range of 0.01 to 0.09 Hz and 0.12 to 0.19 Hz.
## 4 ADMM-Based Distributed Solution Strategy
### Problem Transformation  
To elucidate the product relationship between the variables \(z_{gm}\) and \(x_{mj}\) in constraint C5, this section introduces a novel variable \(f_{gm}^{j}=z_{gm}x_{mj}\). This variable denotes whether user \(m\) selects a video with resolution level \(g\) and connects via access point \(j\). Consequently, constraint C5 is reformulated as follows:  
\[\sum_{p_{mn}^{k}\in\mathcal{P}_{m}}a_{mj}r_{mn}^{k}=\sum_{g=1}^{G}f_{gm}^{j}v_{ g},\quad\forall m\in\mathcal{M},j\in\mathcal{J} \tag{11}\]  
Upon the introduction of a new variable \(f_{gm}^{j}\), the numerical constraint of C2 remains unaltered; however, the focus shifts from users selecting a single access base station to each user picking one access base station specifically for video streams at resolution level \(g\). The constraint concerning variable \(x_{mj}\) within C2 is thus reformulated as follows:  
\[\sum_{j\in\mathcal{J}}\sum_{g=1}^{G}x_{mj}z_{gm}=1,\quad\forall m\in\mathcal{M} \tag{12}\]  
This adjustment, centered around the newly defined variable \(f_{gm}^{j}=z_{gm}x_{mj}\), modifies constraint C2 further into:  
\[\sum_{j\in\mathcal{J}}\sum_{g=1}^{G}f_{gm}^{j}=1,\quad\forall m\in\mathcal{M} \tag{13}\]  
Both are subsequently relaxed to take values in the interval \([0,1]\). Consequently, constraint C8 is updated to:  
\[z_{gm},f_{gm}^{j}\in[0,1],r_{mn}^{k},p_{l},y_{mn}\in\Re^{+} \tag{14}\]  
With these transformations, the original optimization problem **P0** is converted into a new problem, denoted as **P0\({}^{\prime}\)**, expressed as:\[\mathbf{P0^{\prime}}: \min_{\begin{subarray}{c}\text{Z,F,Y,R,P}\\ \text{s. t.}\end{subarray}} -\frac{1}{|\mathcal{M}|}\sum_{m\in\mathcal{M}}\sum_{g=1}^{G}s_{g}z_{ gm}\] (15) s. t. \[\text{C1}: \sum_{g=1}^{G}z_{gm}=1,\quad\forall m\in\mathcal{M}\] \[\text{C2}: \sum_{j\in\mathcal{J}}\sum_{g=1}^{G}f_{gm}^{j}=1,\quad\forall m \in\mathcal{M}\] \[\text{C3}: \sum_{\mathbf{p}_{mn}^{k}\in\mathcal{P}_{mn}}r_{mn}^{k}=h_{mn}y_ {mn},\quad\forall m\in\mathcal{M},n\in\mathcal{N}\] \[\text{C4}: \sum_{g=1}^{G}z_{gm}c_{g}=\sum_{n\in\mathcal{N}}y_{mm},\quad \forall m\in\mathcal{M}\] \[\text{C5}: \sum_{\mathbf{p}_{mn}^{k}\in\mathcal{P}_{m}}a_{mj}r_{mn}^{k}= \sum_{g=1}^{G}f_{gm}^{j}v_{g},\quad\forall m\in\mathcal{M},j\in\mathcal{J}\] \[\text{C6}: EPFD_{j}\leq EPFD_{thr},\quad\forall j\in\mathcal{J}_{geo}\] \[\text{C7}: \sum_{m\in\mathcal{M}}y_{mm}\leq C_{n},\quad\forall n\in \mathcal{N}\] \[\text{C8}: \sum_{l\in\mathcal{L}_{n}}2^{\hat{l}}\leq P_{n},\quad\forall n\in \mathcal{N}\] \[\text{C9}: \sum_{\mathbf{p}_{mn}^{k}\in\mathcal{P}_{l}}a_{l}r_{mn}^{k}\leq B _{l},\quad\forall l\in\mathcal{L}_{wd}\] \[\text{C10}: \sum_{\mathbf{p}_{mn}^{k}\in\mathcal{P}_{l}}a_{l}r_{mn}^{k}\leq \hat{R}_{l}^{\max},\quad\forall l\in\mathcal{L}_{wl}\] \[\text{C11}: z_{gm},f_{gm}^{j}\in[0,1],r_{mn}^{k},\hat{p}_{l},y_{mn}\in \Re^{+}\]
# Optimization of Pickup Vehicle Scheduling for Steel Logistics Park with Mixed Storage
## 3 Pickup Vehicle Scheduling Model
### Mathematical Model for the PVSP-MSS  
This study presents the MILP model for the PVSP-MSS with two objective functions. The MILP model is described as follows:  
\[\begin{array}{ll}\min\mathbf{F_{1}}=C_{\max}=max\{C_{i,n_{i}}\mid\forall i\in I\} \\ \min\mathbf{F_{2}}=SC_{\max}=max\{C_{i,n_{i}}-S_{i,1}\mid\forall i\in I\}\\ \end{array} \tag{2}\]  
where \(\mathbf{F_{1}}\) indicates the minimization for the makespan of the steel logistics park and \(\mathbf{F_{2}}\) indicates the minimization for the maximum makespan of all vehicles. It is worth noting that \(\mathbf{F_{1}}\) has an implicit conflict with \(\mathbf{F_{2}}\). In other words, blindly reducing \(\mathbf{F_{2}}\) might lead to a sharp increase in \(\mathbf{F_{1}}\) and vice versa. Thus, the PVSP-MSS is multi-objective optimization problem subject to:  
\[S_{i,j}+t_{i,j,k}+L\big{(}X_{i,j,k}-1\big{)}\leq C_{i,j},\forall i \in I,j\in J_{i} \tag{4}\] \[C_{i,j}\leq S_{i,j+1},\forall i\in I,j\in\{1,\ldots,n_{i}-1\}\] (5) \[S_{p,q}\geq S_{i,j}+t_{i,j,k}-L(3-y_{i,j,p,q,k}-X_{i,j,k}-X_{p,q, k}),\] \[\forall i\in I,j\in J_{i},p\in I,q\in J_{p},k\in U_{i,j}\cap U_{ p,q}\] (6) \[S_{i,j}\geq S_{p,q}+t_{i,j,k}+L(2+y_{i,j,p,q,k}-X_{i,j,k}-X_{p,q, k}),\] \[\forall i\in I,j\in J_{i},p\in I,q\in J_{p},k\in U_{i,j}\cap U_{ p,q}\] (7) \[\sum_{k=1}^{u_{ij}}X_{i,j,k}=1,\forall i\in I,j\in J_{i}\] (8) \[S_{i,j}\geq 0,\forall i\in I,j\in J_{i}\] (9) \[t_{i,j,k}\geq 0,\forall i\in I,j\in J_{i},k\in U_{i,j}\] (10) \[C_{i,j}\geq 0,\forall i\in I,j\in J_{i} \tag{11}\]  
Constraint (4) ensures that the completion time of vehicle operations is greater than or equal to the sum of the start time of operations and the pickup time. Constraint (5) ensures that start time of the next operation is greater than or equal to the completion time of the current operation. Constraints (6) and (7) ensure that a yard can only serve one vehicle at a time. Constraint (8) ensures that each vehicle operation can only be a pickup operation in one optional yard at a time. Constraints (9)-(11) are value range limitations.  
The model is a typical mixed-integer linear programming model with NP-hard characteristics. To test the proposed model, GUROBI 10.0.1 based on the python programminglanguage is implemented to solve some instances. The experiments in Section V show that the complexity of the problem is so high that GUROBI cannot find an optimal solution in the desired time.
## 4 Methodology
### Thermal Error Modeling  
#### 4.2.1 RBF Neural Network  
RBF neural network is a three-layer feedforward neural network proposed at the end of the 1980s, and its network structure is shown in Figure 5.  
The basic principle of RBF neural network is to map the linearly inseparable sample of low-dimensional space to high-dimensional hidden layer space without using weight connection and make the high-dimensional space become linearly separable [55]. The mapping between the hidden layer and the output space is linear. In other words, the network output is the linear weighted sum of the hidden unit output. It can be seen that the network output is linear with respect to tunable arguments, so that the network weight can be resolved directly from the system of linear equations, thus greatly accelerating the learning speed and preventing local minimum problem.  
Assume that RBF neural network input is \(X=\{x_{1},x_{2},\ldots,x_{n}\}\) (where \(X\)\(\in\)\(R^{n}\)), thus, the hidden layer output \(Z_{j}\), can be obtained using Equation (6):  
\[Z_{j}=R_{j}(\parallel X-C_{j}\parallel) \tag{6}\]  
where, \(R_{j}\) () is the RBF, and \(C_{j}\) is the center of the \(j\)th basis function.  
Generally, \(R_{j}\) is set as the \(n\)-dimensional Gaussian kernel function, as shown in Equation (7).  
\[R_{j}(\parallel X-C_{j}\parallel)=exp\left(-\frac{\parallel X-C_{j}\parallel ^{2}}{\sigma_{j}^{2}}\right) \tag{7}\]  
where, \(\sigma_{j}^{2}\) is the square of the width of the \(j\)th basis function.  
Through the linear correlation between hidden layer and output layer, the output of RBF neural network \(Y\) can be obtained using Equation (8).  
\[Y=\sum_{j=1}^{m}W_{j}Z_{j} \tag{8}\]  
where, \(W_{j}\) is the weight between hidden layer and output layer of RBF neural network; and \(m\) is the number of hidden layers.  
Additionally, the sum of the differences between the actual output and the expectation of the RBF neural network is utilized as the fitness function in the PSO algorithm, as shown in Equation (9).  
\[Fit=\sum_{i=1}^{n}(D_{i}-Y_{i}) \tag{9}\]  
where, \(n\) is the iterations; \(D_{i}\) is the expected value in the \(i\)th iteration; and \(Y_{i}\) is the actual output of the RBF neural network in the \(i\)th iteration. The lower the \(Fit\) value, the better the optimization effect.  
At present, the RBF neural network has caught scholars' attention in many fields due to its simple structure and excellent approximation characteristics. It is mostly applied in classification mode [56], data mining [57] and other fields [58; 59; 60]. Compared with the BP neural network, the RBF neural network has better generalization ability and stronger  
Figure 5: RBF neural network structure diagram.  
global approximation to nonlinear functions. In addition, the RBF neural network has fast learning speed and the ability to avoid local extremum. Therefore, in this paper, RBF neural network is utilized to construct the thermal error model for the spindle of CNC machine tool.
###### Abstract  
The \"Kokyar Greening Project\" in the suburb of Aksu, Xinjiang, is a model of large-area artificial afforestation in an environment of drought and water scarcity. As an important part of the \"3-North Shelter Forest Program\", it plays an important role in promoting the economic development and the environmentally friendly construction of Aksu and even of the whole Xinjiang region. Based on multisource remote-sensing data and meteorological observation data, this study explored the temporal and spatial changes in the vegetation parameters (FVC, NPP, and VEQI) and the ecological parameters (RSEI and LULC) in the Kokyar Project Area from 2000 to 2021. Based on the Theil-Sen median and TSS-RESTEND, this study investigated the path of mutual influence among the FVC, NPP, VEQI, and RSEI, as well as their responses to climate change and human activities. The results show that (1) from 2000 to 2021, the FVC, NPP, VEQI, and RSEI in the Kokyar Project Area showed a significant upward trend and showed the distribution characteristics of \"high in the south and low in the north\". (2) Over the past 22 years, the RSEI has shown a significant increase with the FVC, NPP and VEQI (\(p<0.001\)), indicating that the \"Kokyar Greening Project\" has achieved significant ecological benefits. (3) The changes in the vegetation parameters and RSEI in the Kokyar Project Area were dominated by human activities. (4) The Kokyar Project Area has caused great changes to the ecosystem pattern of the region, and the vegetation parameters and RSEI in the Kokyar Project Area have increased, mainly in the form of cropland and grassland expansion over the past 22 years.  
Kokyar greening project; net primary productivity; fractional vegetation cover; vegetation ecological quality index; remote-sensing ecological index; land use and land cover +
_Sustainability_
## 1 Introduction
The evidence is scarce regarding the effectiveness of low-volume RT exercise on the biomarkers of muscle health, metabolic profile, and oxidative stress in cognitively impaired individuals. Therefore, considering the existing research gaps, the current study aimed to investigate the impact of an 8-week low-volume RT regimen on several serum biochemical markers in individuals with cognitive impairment. Specifically, this study evaluated muscle damage upon exercise using markers such as creatine kinase (CK) and lactate dehydrogenase (LDH). Furthermore, we investigated the activity of liver transaminases to monitor liver function [30; 31], a crucial aspect given the frailty of the target population of this study [32]. Finally, we investigated the impact of a low-volume RT regimen on their lipid profile (low-density lipoprotein (LDL) and high-density lipoprotein (HDL) cholesterol, total cholesterol, atherogenic index, and triglycerides), glycemic control parameters (glycated hemoglobin and estimated average glucose), and oxidative stress. We hypothesized that an 8-week low-volume RT intervention would lead to improvements in metabolic and oxidative stress biomarkers in the institutionalized individuals with cognitive impairment. Specifically, we expected increases in the amount of HDL cholesterol, reductions in the total and LDL cholesterol amounts, and a decrease in the atherogenic index, with no significant changes in the markers of liver function (AST and ALT), muscle damage (CK and LDH), and oxidative stress (MDA). If confirmed, our findings would support the safety and efficacy of low-volume RT in this vulnerable population.