esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Mathematical Models Used for Route Development
### Environmental Forces Originating from Wind  
The mathematical model of wind acting on the ship consists of Equations (1)-(3). These equations are used to determine the effect of wind pressure on each axis of the vessel. In the calculations, it is assumed that the wind acts on the midpoint of the ship's Length Between Perpendiculars (\(L_{pp}/2\)).  
\[Wind_{X}=q_{w}\cdot C_{x}(\varphi_{w})\cdot A_{FW} \tag{1}\]  
\[Wind_{Y}=q_{w}\cdot C_{Y}(\varphi_{w})\cdot A_{LW} \tag{2}\]  
\[Wind_{M}=q_{w}\cdot C_{M}(\varphi_{w})\cdot A_{FW}\cdot H_{FW} \tag{3}\]  
where \(Wind_{X}\) --wind force acting on the \(X\)-axis of the ship [N]; \(q_{w}\)--the factor determining the wind pressure on the ship's hull [kg/m\(\cdot\)s\({}^{2}\)]; \(\varphi_{w}\)--angle of wind impact relative to the \(L_{pp}/2\) point [\({}^{\circ}\)]; \(C_{X}\)--shape coefficient for the \(X\)-axis [-]; \(A_{FW}\)--cross-sectional area of the above-water hull [m\({}^{2}\)]; \(Wind_{Y}\) --wind force acting on the \(Y\)-axis of the ship [N]; \(C_{Y}\)--shape coefficient for the \(Y\)-axis [-]; \(A_{LW}\)--lateral surface area of the hull above the waterline [m\({}^{2}\)]; \(Wind_{M}\)--torque generated by wind force [N\(\cdot\)m]; \(C_{M}\)--shape coefficient for the torque [-]; \(H_{FW}\)--offset of the centroid of the lateral above-water surface relative to the \(L_{pp}/2\) point [m].  
The factor determining the wind pressure on the ship's hull:  
\[q_{w}=\frac{1}{2}\cdot\rho_{a}\cdot V_{W}^{2} \tag{4}\]  
where \(\rho_{a}\)--air density [kg/m\({}^{3}\)]; \(V_{w}\)--wind speed [m/s].  
The hull shape coefficients should be determined using the following Equations (5)-(8), which come from [33]:  
\[C_{X}=-CD_{IAF}\cdot\frac{\cos(\varphi_{w})}{1-\frac{\delta}{2}\Big{(}1-\frac{ CD_{l}}{CD_{l}}\Big{)}\sin^{2}(2\varphi_{w})} \tag{5}\]  
\[C_{Y}=CD_{I}\cdot\frac{\sin(\varphi_{w})}{1-\frac{\delta}{2}\Big{(}1-\frac{ CD_{l}}{CD_{l}}\Big{)}\sin^{2}(2\varphi_{w})} \tag{6}\]  
\[C_{M}=\bigg{[}\frac{H_{FW}}{L_{oa}}-0.18\Big{(}\varphi_{w}-\frac{\pi}{2}\Big{)} \bigg{]}\cdot C_{Y}(\varphi_{w}) \tag{7}\]  
\[CD_{l}=CD_{IAF}(\varphi_{w})\cdot\bigg{(}\frac{A_{FW}}{A_{LW}}\bigg{)} \tag{8}\]  
where \(C_{X}\)--shape coefficient for the \(X\)-axis [-]; \(CD_{IAF}\)--longitudinal resistance coefficient [-], \(\varphi_{w}\)--wind angle [\({}^{\circ}\)]; \(\delta\)--cross-force parameter [-]; \(CD_{l}\)--longitudinal resistance [-]; \(CD_{l}\)--transverse resistance [-]; \(C_{Y}\)--shape coefficient for the \(Y\)-axis [-]; \(C_{M}\)--shape coefficient for torque [-]; \(H_{FW}\)--offset of the centroid of the lateral above-water surface relative to the \(L_{pp}/2\) point [m]; \(L_{oa}\)--length overall [m]; \(A_{LW}\)--lateral surface area of the hull above the waterline [m\({}^{2}\)]; \(A_{FW}\)--cross-sectional area of the above-water hull [m\({}^{2}\)].  
The following figure (Figure 2) presents the values of the hull shape coefficient depending on the wind impact angle.
## 3 Results and Discussion
### Host Tracking of ARGs
The reason that pathogens were the major hosts of ARGs in Xinjiang WWTPs may be due to two reasons: WWTPs contain significant loads of human pathogens which may spread to surrounding rivers and soil [56; 57], and pathogens with no ARGs tend to acquire them under antibiotic selective pressure, while pathogens previously containing ARGs preferentially survive and are enriched in the population [57]. Pathogens carrying ARGs may cause multiple-disease resistance and pose a more serious threat to human health than non-pathogenic bacteria [9]. _E. coli_ was the most abundant ARP in CJ WWTP and accounted for 15.4% (Figure S4A). _P. aeruginosa_ and _K. pneumoniae_ were the presumed hosts of three PARGs (_class A beta-lactamase_, _qac_Edelta1 and _sul_1) in CJ WWTP which is consistent with HX WWTP (Table S2). Furthermore, _K. pneumoniae_ and _P. aeruginosa_ persist in the influent, activated sludge, and effluent of HX and CJ WWTPs (Figure S5) which is consistent with other WWTPs [48]. This indicated that these two ESKAPE pathogens have a high probability of entering the receiving environment and promoting the spread of ARGs. More effective effluent disinfection methods should be studied to prevent ARB and ARPs from entering the receiving environment, especially considering their regeneration and reactivation [58; 59]. Meanwhile, a few non-pathogenic species showed multidrug resistance and resistance to bacitracin: _Acidoorax_ sp. 1608163 and _Acidoorax_ sp. KKS102 (Figures 3 and S4). Non-pathogenic environmental species are important ARG carriers and the potential risk of these species to the spread of antibiotic resistance needs further evaluation [47].
## 4 End to End Security Analysis on the Proposed IIoT Security Architecture
### Transport Layer  
Transport Layer mainly provides ubiquitous connectivity for \"things\" and transmitting generated data from the device layer to the processing layer via a heterogeneous collection of communication networks, as illustrated in Figure 2. This section analyses the security of the transport layer based on its range of coverage and functional architecture, mainly capillary network, backhaul network and backbone network.
## 3 Data and Methodology  
### Data  
Three types of medical imaging datasets are used in the experiments, including OCTs of retinal diseases, X-rays of pneumonia, and color fundus images of glaucoma. Table 1 summarizes the subset of these three datasets used in the experiments. The retinal diseases dataset comprises four classes, i.e., normal, choroidal neovascularization (CNV), diabetic macular edema (DME), and drusen. The pneumonia dataset contains two classes: normal and pneumonia. Similar to the pneumonia dataset, the glaucoma dataset includes normal and glaucoma as the two classes. All three datasets are open source. The retinal diseases and pneumonia dataset can be downloaded at [[https://data.mendeley.com/datasets/rscbjb79](https://data.mendeley.com/datasets/rscbjb79) js/3]([https://data.mendeley.com/datasets/rscbjb79](https://data.mendeley.com/datasets/rscbjb79) js/3), accessed on 1 January 2022, and the glaucoma dataset can be downloaded at
# High-Performance Flux Tracking Controller for Reluctance Actuator
## 1 Introduction
In this article, taking into account the effect of displacement on the uptake of model parameters, the sliding mode controller based on the disturbance observer was used to compensate for the unmodeled dynamics, displacement disturbances, and model identification errors in linear systems. The simulation results show that the proposed control strategy makes the system have excellent tracking performance. In general, the specific contributions of this article are as follows:  
(1) To identify the parameters of the Hammerstein model of the reluctance actuator, the frequency turning point, which is the frequency inflection point between rate-independent and rate-dependent in the reluctance motor model, was determined for the first time. From the result of identification, we can draw a conclusion that the Hammerstein structure has an excellent effect in modeling rate-dependent hysteresis;  
(2) Different from the current reluctance motor research methods, this paper considers the coupling effect of eddy current and displacement on the hysteresis model, which leads to a rate-dependent and displacement-dependent hysteresis effect in the reluctance actuator. Specifically, the Hammerstein model is used to represent the rate-dependent hysteresis, and the displacement-dependent hysteresis effect is regarded as the disturbance of the system;  
(3) Compared to existing control methods, the merit of using the novel control strategy lies in that by prespecifying a Hammerstein structure for the reluctance actuator, there are not any simplifications or linearization in dealing with the nonlinear hysteresis, while the controller design is based on the linear rate-dependent part of the reluctance actuator only;  
(4) The simulation results show that the proposed control strategy has good robustness to the unmodeled dynamics (the perturbation of displacement to the hysteresis model) and external disturbances of the reluctance motor. In other words, the proposed control strategy has perfect flux tracking ability. The rest of this paper is organized as follows: According to Hammerstein's structure, the rate-dependent model of the reluctance motor is established in Section 2; it should be indicated that the hysteresis inverse model based on the Bouc-Wen model is obtained. In Section 3, considering the model error and disturbance, the discrete sliding mode control rate based on the disturbance observer is designed. The simulation analysis is given in Section 4. Specifically, it includes the verification of the Hammerstein structure and the feasibility analysis of the proposed control strategy. In addition, inverse model compensation and PI composite strategy, inverse model compensation, and discrete sliding mode composite strategy are carried out. The simulation results show that the proposed control strategy has better performance than the other two control strategies. Afterward, conclusions are addressed in Section 5.
# An Enhanced Adaptive Block Truncation Coding with Edge Quantization Scheme +
## 4 Experiment and Comparison
### Experimental Results
Scheme C depicts the improvement in the visual quality near edges, and its edge image is very similar to the original image.
## 5 Conclusions and Further Remarks
### Conclusions
By 2018, three relevant research papers were published, and they all used wearables and IMU-based MoCap systems. Though these systems are costly and can annoy human workers during manual tasks, they produce reliable motion data. Moreover, the collected data can be processed in relatively simple ways without complicated algorithms and high computing powers. In contrast, a single camera is generally inexpensive compared with MoCap systemsregardless of whether it is a depth type or not, and it does not use any tiny devices or markers that need to be attached to the human body or clothing. Analyzing the image or video data produced by a camera using traditional techniques is difficult, but modern computer hardware and AI algorithms enable pre-processing procedures such as object detection, instance segmentation, and human motion recognition to facilitate these analyses. Consequently, cameras and AI algorithms are expected to be more widely applied to develop human-focused DT applications in the future. Fifth, a visualization tool must be carefully chosen and planned. For instance, the location of a human worker, represented as a symbol or an icon, can be visualized using relatively simple tools. Such real-time location information can be intuitively understood and can provide additional information, such as the number of human workers in a dangerous area. In contrast, more complicated forms of visualization such as a whole body avatar require more specialized visualization tools. Note that a whole body avatar is the most popular form of visualization for human workers in relevant applications. Developing custom applications that provide good-looking visual representations may be time-consuming and may prolong the entire DT application development period. Hardware vendor-provided software and commercial human motion simulation software are also available for the visual representation of the human workers; however, they can be considerably expensive and hard to customize. Therefore, an appropriate visualization tool is a key component for successfully developing a human-focused DT application for OSH. Sixth, only about a third of existing applications utilize simulations, though many researchers pointed out that simulation is an important feature of DT applications and CPS. Most of existing applications focus on human motion simulations using commercial human motion simulation software; however, the simulation modeling and experiment procedures are generally not described in detail. One reason for the low popularity of simulation is that traditional observational methods can be applied without simulation modeling and experiments. Moreover, developing a custom application enabling simulation modeling and experiment is non-trivial; commercial simulation software may be a practical alternative, but commercial human motion simulation software is often costly, which is thought to be another cause of the low popularity of simulations.
# Multi-Year Monitoring of Ecosystem Metabolism in Two Branches of a Cold-Water Stream
## 3 Results
### Ecosystem Metabolism

Table 4: ANCOVAs of In(water temperature) on \(\ln\)(GPP), In(ER), and In(NEP) for the North Branch and South Branch of Valley Creek, MN. Figure 7: Relationship between water temperature or photosynthetically active radiation (PAR) on measures of ecosystem metabolism—GPP, ER, and NEP. Lines are best-fit lines, and shaded areas are 95% confidence limits. Since there were no discharge data for the North Branch for 2018 and May-early July 2019, the relationship between discharge and ecosystem metabolism was examined separately for each location. There was a small but negative impact of discharge on GPP in the South Branch but no impact in the North Branch (Figure 8). In both locations, discharge was positively correlated with ER, resulting in significant negative relationships between discharge and NEP (Figure 8). The R\({}^{2}\) values were quite low for these regressions, indicating that while in most cases there was a significant influence of discharge on various components of stream metabolism, the effect was quite modest, at most explaining 32% of the variability in NEP and often explaining less than 20% (Figure 8).
# Multi-Object Detection for Inland Ship Situation Awareness Based on Few-Shot Learning
## 6 Experimental Results and Discussion
### Experiments on Multi-Object Detection and Tracking for Inland Ships  
In the following experiment, we combined the object tracking algorithm DeepSORT to carry out the test and analyzed the effectiveness of the proposed framework under good visibility and restricted visibility. As cargo ships have the largest proportion among the ships sailing on the Yangtze River, we consider cargo detection accuracy and recall rate to be the most important indicators. Therefore, while ensuring a certain level of detection accuracy for other categories, we ultimately chose the ShuffleAttention + transfer learning strategy model with the highest detection accuracy for cargo ships for our multi-object detection and tracking experiments.
# Spectral Correction of CPV Modules Equipped with GaInP/GaInAs/Ge Solar Cells and Fresnel Lenses
## 2 Materials and Methods  
### The Spectral Factor in MJ-Based CPV Devices  
The short-circuit current density (\(J_{sc,i}\)) of each subcell of a MJ solar cell in a concentrator can be expressed as:  
\[J_{sc,i}=CR\cdot\int_{\lambda_{i,min}}^{\lambda_{i,max}}SR_{i}(\lambda)\cdot \eta_{opt}(\lambda)\cdot E_{b}(\lambda)d\lambda \tag{1}\]  
where \(CR\) is the geometrical concentration ratio, \(\lambda_{i,min}\) and \(\lambda_{i,max}\) the wavelength range of the corresponding subcell, \(SR_{i}\) the spectral response, \(\eta_{opt}\) the optical efficiency, \(E_{b}(\lambda)\) the spectrally resolved direct normal irradiance and the \(i\)-index corresponds to the specific junction of the MJ solar cell.  
Due to the series connection of different junctions, the current density of the whole MJ solar cell is given by the minimum current:  
\[J_{sc,cell}=\min(J_{sc,i}) \tag{2}\]  
The impact of the spectrum is quantified using the \(SF\) of each individual subcell, which is expressed as:  
\[f_{s,i}=SF_{i}=\frac{\int E_{b}(\lambda)\cdot\eta_{opt}(\lambda)\cdot SR_{i}( \lambda)d\lambda}{\int E_{b,ref}(\lambda)\cdot\eta_{opt}(\lambda)\cdot SR_{i} (\lambda)d\lambda}=\frac{J_{sc,i}}{E_{b}}\frac{E_{b,ref}}{J_{sc,i}^{ref}} \tag{3}\]  
where \(f_{s}\) is the spectral function and the index _ref_ indicates the reference conditions.  
Bearing the above in mind, the \(SF\) of the whole MJ solar cell can be expressed by means of the following equation:  
\[f_{s}=SF=min\left(SF_{i}\cdot\frac{J_{sc,i}^{ref}}{J_{sc,cell}^{ref}}\right) \tag{4}\]  
This index quantifies the spectral impact on the performance of a MJ CPV device as a function of the input spectral distribution. In this sense, a value greater than 1 indicates spectral gains, a value lower than 1 indicates spectral losses and a value equal to 1 indicates spectral conditions equivalent to the reference spectrum. This index has been experimentally demonstrated to be valid for approximating the spectral impact on the power or energy output of MJ CPV systems under real operating conditions [11].
### Literature Review  
#### 1.2.1 Research Status of IWSNs  
The rapid development of industrial manufacturing has produced many meaningful process transformation schemes [2; 3]. Meanwhile, there are also many valuable theories and methods for the research of IWSNs. [PERSON] et al. [4], aiming at disseminating industrial wireless networks and the limitation of sensors themselves, proposed a robust and scalable mechanism to accurately and effectively detect negative anomalies. [PERSON] et al. [5] detailed the design goals, challenges, and solutions of IWSNs, conducted a comprehensive review of existing standards and industry protocols, and critically assessed the potential of these standards and protocols. Available hardware platforms, specific industrial energy harvesting technologies, and capabilities are discussed in detail. [PERSON] et al. [6] discussed the design challenges of cluster-based schemes, important cluster formation parameters, and classification of hierarchical clustering protocols. Furthermore, existing cluster-based and grid-based technologies are evaluated by considering specific parameters to help users choose an appropriate technology. In [PERSON] et al. [7], several clustering methods are studied to demonstrate their advantages and disadvantages.  
In the field of IWSNs, distributed wireless sensors have been widely studied and have solved a large number of real-world problems. For example, [8] proposed the first distributed MAC protocol to minimize the access delay by optimizing the window size in the power competition. This method has better access delay and packet arrival rate by simulation. Furthermore, [9], aiming at the problem of signal mixing caused by a complex industrial environment, proposed a signal classification method based on feature signal fusion, which performs down-frequency and sampling preprocessing on the signal received by the node to obtain an intelligent representation of the movement. [PERSON] et al. [10] proposed distributed congestion control by duty-cycle restriction to detect and mitigate DoS in the Industrial Internet of Things. The ability to reduce detection and mitigation times can be compared to centralized approaches. For the deployment problem of a 3D industrial space with obstacles, [11] proposed a distributed parallel particle swarm optimization to maximize the coverage and prolong the life cycle. [PERSON] et al. [12] reported and analyzed distributed systems for industrial sensor and control applications. They discussed two aspects: wireless sensor network localization and node self-organizing clustering.  
Most of these distributed WSNs consider two aspects: (1) the optimization of network topology [11; 12] and (2) the optimization of network routing protocols and algorithms [8; 9; 10]. However, the cascading failures caused by dependencies between network devices are ignored in these studies. The phenomenon of cascading failures is catastrophic, so from the perspective of complex networks, understanding the causes of cascading failures and modeling their behavior and effects is crucial and irreplaceable to ensure the reliable operation of network systems [1].
## 3 Results and Discussion
### BBE Applied to Dried R. typhina Fruits  
The parameter values for the UAE, which were optimized based on the Box-Behnken design, namely a solvent concentration of 61.51% and a solvent/solid ratio of 20/1, were further used in BBE experimental runs, this time varying the extraction time and bead size and material (Table 9). The results of the contents of the analyzed bioactive compounds under different experimental BBE conditions are shown in Table 9.  
Regarding the influence of size, ceramic beads of 2.8 mm were found to be more suitable for the extraction of higher amounts of anthocyanins, phenolics, and carotenoids at 5 or 10 min. The 2.4 mm metal beads gave better results for the tann content. The results presented in the form of Boxplot representations (Figure 6) indicate that the mean values of TAC, TTC, and carotenoids were significantly different depending on the bead sizes (\(p\) = 0.05, 0.012, and 0.011, respectively) and bead material type, ceramics or metal (\(p\) = 0.054, 0.012 and 0.013, respectively). TAC was higher when extracted with 2.8 mm beads compared to 2.4 mm beads (\(p\) = 0.0428) or extraction without beads/control (\(p\) = 0.0525). The ceramic beads were more efficient than the metal ones (\(p\) = 0.0658) or control (\(p\) = 0.0805) for the extraction of bioactive compounds. TTC was significantly higher when extracted with 2.4 mm beads (\(p\) = 0.0089 *) and with metal beads (0.045 *) than the control and was marginal-significantly lower when extracted with 1.4 mm beads compared to 2.4 mm beads (\(p\) = 0.0777) and control compared to 2.8 mm beads (\(p\) = 0.0777). The  
Figure 5: Response surface plots (3D) showing the effects of (**1a–5a**) solvent concentration and extraction time, (**1b–5b**) solvent concentration and solvent/solid ratio, and (**1c–5c**) extraction time and solvent/solid ratio, on the content of bioactive compounds and antioxidant activities. carotenoid content was also found to be significantly higher when extracted with 2.4 mm (\(p\) = 0.0113 *), 2.8 mm (\(p\) = 0.0280) beads and metal beads (\(p\) = 0.0057 *) than controls, and marginally significantly higher for ceramic beads compared to controls (\(p\) = 0.0535). The results for the influence of BBE conditions on the antioxidant activities (FRAP and DPPH) of the ethanolic extracts of _R. typhina_ fruits are summarized in Table 10.  
Figure 6: Boxplot representations of TAC, TTC, and carotenoids of _R. typhina_ fruit extracts according to different BBE parameters (bead size, bead material); only boxplot representations of variables with significant mean differences were included. Distinct letters (a, b) on top of the boxes indicate significant differences between values.