esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The urban water environment, an integral component of the terrestrial hydro-sphere, is closely linked to human activities and serves as a fundamental resource for industrial and agricultural development. Sedimentary organic matter in water bodies contains rich biological, physical, and chemical information, playing a central role in nutrient cycling and serving as a primary reservoir for nutrient accumulation. This study assesses the water quality, chemical indicators, and sediment productivity of four typical urban water bodies (Canal, Pond, Lake, and River) in Shaoxing City, eastern China. The results show that artificial water bodies, particularly canals, have higher dissolved oxygen (DO) compared to natural water bodies. Stationary water bodies, such as lakes and ponds, generally have higher total dissolved solids (TDS) and electrical conductivity (EC) than flowing water bodies like rivers and canals. All four urban water body types slightly exceed China's Class-V water quality standard for total nitrogen (TN), with canals, lakes, ponds, and rivers averaging 1.29, 1.22, 1.23, and 1.23 times the standard, respectively. Ponds exhibit the highest total dissolved nitrogen (TDN) content, while ammonium (NH4\({}^{+}\)-N) and nitrate (NO3\({}^{-}\)-N) levels are relatively consistent across the bodies, except for lower NO3\({}^{-}\)-N in lakes. Higher organic matter in canals and lakes, indicated by chlorophyll-a (Chl-a) and permanganate index (CODMn), suggests greater eutrophication compared to ponds and rivers. Sediment total organic nitrogen (TON) content is relatively uniform across all water bodies, with slightly higher values in lakes and rivers. Total organic carbon (TOC) content is highest in lake sediments, 1.51 times that of canals. Carbon/nitrogen (C/N) ratios vary, with ponds and lakes showing the highest averages. Source quantification using isotopic analysis (\(\delta^{13}\)C and \(\delta^{15}\)N values) indicates that phytoplankton is the primary sedimentary organic matter source in rivers and canals, while terrestrial sources are significant in lakes and ponds. Sewage notably contributes to rivers and canals. These findings highlight the need for targeted pollution control strategies, focusing on phytoplankton and sewage as key sedimentary organic matter sources to mitigate eutrophication and enhance water quality in urban environments.  
urban water environment; water quality parameters; water chemical indicators; sedimentary organic matter; carbon and nitrogen isotope; source quantification 2025
# RIS-Assisted High-Speed Communications with Time-Varying Distance-Dependent Rician Channels
## 3 Received Power Maximization and Doppler Shift Alignment
### Doppler Shift Alignment  
Considering the optimal phase shift \(\phi_{mn}^{\text{opt}}(t)\) in (23), recall the DS \(f_{0}(t)\) in (3) and \(f_{mn}(t)\) in (12), and note that \(d(\zeta_{mn}(t))/dt=0\), we have  
\[f_{mn}(t) =-f_{c}\frac{d}{dt}\bigg{(}\tau_{mn}^{\text{MR}}(t)+\frac{\phi_{ mn}^{\text{opt}}(t)}{2\pi f_{c}}\bigg{)} \tag{24}\] \[=-f_{c}\bigg{(}\frac{v(x_{0}-x_{mn}+v\cdot t)}{cd_{mn}^{\text{MR} }(t)}+\frac{d}{dt}\big{(}\frac{\phi_{mn}^{\text{opt}}(t)}{2\pi f_{c}}\big{)} \bigg{)}\] \[=-f_{c}\bigg{(}\frac{v(x_{0}-x_{mn}+v\cdot t)}{cd_{mn}^{\text{MR} }(t)}+\frac{v(x_{0}-x_{\text{BS}}+v\cdot t)}{cd_{0}(t)}-\frac{v(x_{0}-x_{mn}+ v\cdot t)}{cd_{mn}^{\text{MR}}(t)}\bigg{)}\] \[=-f_{c}\frac{d(\tau_{0}(t))}{dt}=f_{0}(t).\]  
**Remark 3**: _It can be observed that if the optimal phase shift \(\phi_{mn}^{\text{opt}}(t)\) in (23) is adopted, then \(f_{0}(t)\) is equal to \(f_{mn}(t)\). In other words, although optimizing the phase shift of the RIS cannot remove the DS entirely, it can align them to \(f_{0}(t)\). Accordingly, the carrier frequency of the received signal \(y(t)\) in (22) changes from \(f_{c}\) to \(f_{c}+f_{0}(t)\). Note that \(f_{0}(t)\) can be ignored if \(f_{0}(t)\ll f_{c}\)._  
Consider the case where the direct link is blocked; we have \(h_{d}(t)=0\), and \(h(t)\) in (21) can be rewritten as  
\[\begin{split} h(t)&=h_{\text{RIS}}(t)\\ &=\sum_{m=1,n=1}^{\sqrt{M},\sqrt{M}}h_{mn}(t)\exp\big{(}-\jmath \phi_{mn}(t)\big{)}g_{mn}(t).\end{split} \tag{25}\]  
Accordingly, the received signal \(y(t)\) in (22) can be rewritten as  
\[y(t)=\sum_{m=1,n=1}^{\sqrt{M},\sqrt{M}}h_{mn}(t)\exp\big{(}-\jmath\phi_{mn}(t) \big{)}g_{mn}(t)s\big{(}t-\tau_{mn}(t)-\frac{\phi_{mn}(t)}{2\pi f_{c}}\big{)} +w(t). \tag{26}\]Since \(\tau_{0}(t)=0\), the optimal phase shift \(\phi_{mn}^{\text{opt}}(t)\) in (23) is changed to  
\[\phi_{min}^{\text{opt}}(t)=2\pi f_{c}\big{(}-\tau_{mn}(t)\big{)}+2\pi\xi_{mn}(t), \tag{27}\]  
where \(\xi_{mn}(t)=\big{\lceil}f_{c}\tau_{nm}(t)\big{\rceil}\).  
Based on (27), we can compute the DS \(f_{mn}(t)\) as  
\[\begin{split} f_{mn}(t)&=-f_{c}\frac{d}{dt}\bigg{(} \tau_{mn}^{\text{MR}}(t)+\frac{\phi_{mn}(t)}{2\pi f_{c}}\bigg{)}\\ &=-f_{c}\bigg{(}\frac{v(x_{0}-x_{mn}+v\cdot t)}{cd_{mn}^{\text{MR} }(t)}-\frac{v(x_{0}-x_{mn}+v\cdot t)}{cd_{mn}^{\text{MR}}(t)}\bigg{)}\\ &=0.\end{split} \tag{28}\]  
**Remark 4**.: _As (28) shows, when there is no direct link \(h_{d}(t)\), the RIS can not only align the DS but remove the Doppler spread, i.e., \(|f_{mn}(t)-f_{0}(t)|=0\). This is the so-called Doppler cloaking [15], and the carrier frequency remains to \(f_{c}\) in this case. Note that with the RIS phase shift optimization, the Doppler spread can be removed. However, the DS cannot be totally eliminated unless the direct link vanishes. Thus, DS and Doppler spread are jointly considered in this paper._
## 4 Discussion
Since vertebrates are used for the majority of the research into the interaction between polyphenols and the microbiome, key physiological differences that would impact microbiome development require consideration. The digestive tract of shrimp differs substantially from that of vertebrates; therefore, the interaction with the microbiome may differ as a consequence. For example, shrimp secrete a chitin sheath in the midgut that surrounds the digesta. This sheath is thought to protect the hindgut from bacteria, but it also limits nutrient uptake [44]. Furthermore, the entire digestive process in shrimp occurs at a neutral pH and at a lower temperature than in mammals. In addition to this, the overall gut transit times for food are between 2 and 4 h for peneaid shrimp (reviewed by [45]), versus around 8 h in mammals such as mice [46]. While data are limited, there is some evidence that plant polyphenols can positively affect specific bacteria in shrimp and could potentially play a role in modulating the gut microbiome. For example, the polyphenols present in a green tea extract can inhibit the growth and lethality of the pathogenic bacteria _Vibrio hareyi_ in kuruma shrimp (_Marsupenaus japonicus_) [47]. Some studies have found a direct effect on immune response and function with the addition of a range of plant sources that was linked to improved survival [43]. Given the positive effects on survival, in addition to other production outcomes such as growth rate and FCR, a follow-up study that directly measures any immunomodulating effects would be useful. A further unexplored area in aquaculture nutrition is the ability of polyphenols to chelate iron, which may have a positive effect on iron homeostasis and therefore growth and survival. A study that tracked iron bioavailability in the presence of the sugarcane extract would provide further understanding if this mechanism was involved in the observations collected during this trial [48] It will also be important to assess the performance of this sugarcane extract in the later stages of the animals' development. Small additions of PRSE (~6 g kg\({}^{-1}\) of diet) led to increases in the growth, survival, and feed conversion efficiency of shrimp. Overall, this study demonstrates that plant polyphenics have potential as additives to shrimp feed and validates the need for further research in this area, including research conducted in commercial rearing environments rather than test conditions. Due to the sourcing of the raw material from the existing sugarcane industry, this extract provides a prospective opportunity to extend the sustainability of the aquaculture industry.
## 2 Materials and Methods
### High Accuracy Prediction Model Based on WTA Ensemble Learning
#### 2.4.2 WTA Ensemble Learning Model  
As analyzed above, the overall prediction accuracies of \(P_{e}\) and \(V\) are relatively high, but the prediction accuracies for unstable samples are still very low. In order to improve the conservativeness of the prediction model, the SVM learning machines based on \(P_{e}\) and \(V\) are taken as two sub-classifiers. Then the outputs of the two classifiers are combined by the winner take all (WTA) ensemble learning method. When the prediction result of any sub-learning machine is unstable, the WTA module will determine that the transient process is unstable; otherwise it will be accepted as stable. The input features of sub-learning machine 1 (M1) is \(V\), and the input features of sub-learning machine 2 (M2) is \(P_{e}\). Stable samples have a label of 1 and unstable samples have a label of 0. The principle of WTA ensemble learning model is expressed as Equation (12).  
\[\left\{\begin{array}{l}y_{1}=f_{1}(V)\\ y_{2}=f_{2}(P_{e})\\ y=\min\{y_{1},y_{2}\}\end{array}\right. \tag{12}\]  
The WTA ensemble learning process can also be shown in Figure 8.  
As previously mentioned, the \(V\) and \(P_{e}\) curves reflect the different dynamic characteristics of power systems. In other words, the transient stability prediction results of M1 and M2 can be relatively independent. Assuming that the error rates of M1 and M2 are \(\varepsilon_{1}\) and \(\varepsilon_{2}\) respectively, then the error rate of the WTA model for unstable samples is shown in Equation (13).  
\[\varepsilon_{WTA}=\varepsilon_{1}\times\varepsilon_{2}\ll\min\{\varepsilon_{1 },\varepsilon_{2}\}, \tag{13}\]  
For stable samples, the prediction accuracy \(P_{st}\) of the WTA model is the product of the accuracies \(P_{st}{}^{M1}\) and \(P_{st}{}^{M2}\). For unstable samples, the prediction accuracy \(P_{um}\) of the WTA model is 1 minus the error rate product of the unstable predictions \(\left(1-P_{um}{}^{M1}\right)\) and \(1-P_{um}{}^{M2}\). Therefore, the overall prediction accuracy \(P_{total}\) of the WTA model can be calculated by 1, minus the proportion of the number of samples with incorrect prediction results to the total number of samples \((N_{st}+N_{um})\), as shown in Equation (14).  
\[\left\{\begin{array}{l}P_{st}=P_{st}{}^{M1}\times P_{st}{}^{M2}\\ P_{um}=1-\left(1-P_{um}{}^{M1}\right)\times\left(1-P_{um}{}^{M2}\right)\\ P_{total}=1-\frac{N_{st}\times\left(1-P_{st}\right)+N_{um}\times\left(1-P_{um }\right)}{N_{st}+N_{um}}\end{array}\right., \tag{14}\]  
Figure 8: WTA ensemble learning model.
# A Configurable Dependency Model of a SCADA System for Goal-Oriented Risk Assessment
###### Abstract
The dependency modelling approach in the context of a SCADA system aims to answer the following questions:  
* What is required for achieving _the safe and secure operation of a SCADA system_ (the core goal)? What does the success of this core goal depend upon? * What is the likelihood of achieving the core goal? * What can be done to maximise the likelihood of achieving the core goal? The key contribution of this paper is _a SCADA Dependency Model (SCADA DM)_--a widely applicable industry-agnostic multi-faceted dependency model of a SCADA system which comprises 452 SCADA system dependencies. Our SCADA DM offers a holistic view of a system that is an integration of sub-models developed by stakeholders responsible for the different aspects of a SCADA system and for the systems reliant on it. The SCADA DM we present in this paper was co-designed with domain experts with varying backgrounds following a rigorous knowledge capture methodology and validated by practitioners and academics via focus groups. By following the rigorous well-documented model development process with the involvement of domain experts, we produced a comprehensive, justified and validated model of a SCADA system, which is capable of supporting risk assessment process. The presented SCADA DM facilitates understanding of complex interactions and interdependencies within a SCADA system. The adopted dependency modelling approach offers the ability to predict the impact of failure of any dependency on other parts of the system and on the system as a whole. A SCADA DM assists with managing the dependencies within the system and well as accounting for external factors that are outside of the system control (e.g., geo-environmental factors and espionage). The secondary contribution of the paper is the illustrative example of applying the dependency modelling approach to risk assessment, underpinned by the SCADA DM, in the context of SCADA systems. We focus on a water distribution process. To the best of our knowledge, it is the first application of dependency modelling to assessing cyber security risks within a SCADA system in a CNI sector. The illustrative example shows how the presented generic SCADA DM could be configured for a specific SCADA system and how it could facilitate risk assessment process. The remainder of the paper is organised as follows. In Sections 2 and 3, we provide an overview of dependency modelling and of the related literature, respectively. In Section 4, we discuss the research methodology for the development and validation of the SCADA DM. In Section 5, we present the SCADA DM, focusing on its six key areas. An illustrative example of conducting a risk assessment for a SCADA system controlling water distribution is outlined in Section 6. Finally, conclusions are drawn, and future work is discussed in Section 7.
## 4 Result and Discussion  
### Verification of Model and Experimental Results  
Figure 5 shows the comparison between the simulation value of heat exchange and the experimental value. It can be seen from the comparison that the error between the experimental value and the simulation value is large at low filling rate. When the filling rate range FR is greater than 40%, the simulation value is close to the experimental value, the relative error value is less than 10%, and when the filling rate FR is less than 40%, the relative error is more than 10%. Figure 6 shows the comparison between the simulated and experimental values of the average air temperature at the outlet of the evaporator side, which is similar to Figure 7. Figures 7-10 show the comparison between the simulated values and the experimental values of the refrigerant pressure and temperature at the inlet and outlet of the evaporator and the condenser. In the range of 0%-40% of the liquid filling rate, the error is large, and the maximum relative error is more than 20%. When the liquid filling rate is greater than 40%, the fitting result is good, and the average relative error is less than 10%. According to the analysis of all comparison results, when the filling rate is less than 40% (filling mass \(<\) 0.8 kg), the simulation results are relatively poor. On the one hand, the correlation between heat transfer and pressure drop is not accurate enough when the heat flow density is low, on the other hand, the heat pipe system itself cannot operate normally under the low filling rate, and the temperature and pressure sensors are not available when the operation state is not stable. Accurate temperature and pressure values were obtained by the method. In the actual operation, the system will be filled with refrigerant to the range of the best filling rate, so the model established in this paper is relatively accurate under the actual operation conditions, only for some of the more extreme operating conditions will there be deviation.  
Figure 5: Comparison of experimental and simulated heat transfer values.  
Figure 6: Comparison of air temperature at the outlet of the evaporator.  
Figure 7: Comparison of temperature and pressure of refrigerant at the inlet of the evaporator.  
Figure 8: Comparison of temperature and pressure of refrigerant at the outlet of the evaporator.  
Figure 9: Temperature and pressure comparison of cooling distribution unit (CDU) inlet refrigerant.  
This section is divided by subheadings. It provides a concise and precise description of the experimental results, their interpretation, as well as the experimental conclusions that can be drawn.
## 6 Recent Applications in Metabolic Engineering for Xanthophylls Production
ASX yields reached in the efficient transformantswere up to 34 mg/L instead of 18 mg/L in the wild strain microalgae [115]. Fucoxanthin is another highly researched carotenoid due to its anti-obesity, anti-diabetic and anti-cancer properties. It is also well established as strong antioxidant compounds [29]. With the aim of increasing fucoxanthin production, [PERSON] et al. (2020) transformed _Phaeodactylum tricornutum_ with single plasmids or combinations of them for the overexpression of genes with a putative role in xanthophylls biosynthesis [116]. They obtained two triple transformant genes: Vde-related (VDR),Zeaxanthin epoxidase 3 (ZEP3) and Violaxanthin de-epoxidase (VDE), were over expressed, allowing the carotenoids accumulation with a four-fold increase in the fucoxanthin content compared to the wild strain [116]. Similarly, overexpression of the Psy gene in _Phaeodactylum tricornutum_ allowed a fucoxanthin content 1.45 times higher than that in the wild type strain [117]. In the same way, overexpression of Dxs and Psy genes led to an increase in fucoxanthin content of 2.4 fold and 1.8-fold respectively [117; 118]. Similar metabolic engineering studies were realized for the over production of lutein. _Chlamydomonas reinhardtii_ was transformed using the genes of _Dunaliella salina_ and _Chorella zofnigenisis_, producing respectively 2.6- and 2.2-fold higher yields of lutein [119]. A point mutation was introduced in the endogenous gene encoding PDS of _Chlamydomonas reinhardtii_ to enhance its expression. Concomitantly to it, the increase of lutein production was observed [120]. In a very similar approach using _Chlamydomonas reinhardtii_, the gene encoding for PBS from _Xanthophyllomyces dendorhous_ was introduced into pMS188 plasmid and the nuclear transformed [121]. The mutated microalga possesses a bifunctional enzyme with the two PSY and LCYB activities. This allowed carotenoid accumulation for the first time using an heterologous expression system, leading to a simultaneous increase of about 60% in lutein biosynthesis under low light culture conditions. Random mutagenesis has been effectively used for the production of _Chlorella sorokiniana_ mutants with high contents of lutein [119]. In another study, ethyl methane sulfonate and N-methyl-N\({}^{\prime}\)-nitro-N-nitrosoguanidine have been used as chemical mutagenesis for generating a lutein-deficient _Chlorella vulgaris_ (CvLD), which was found to be an enhanced producer of the pigment violaxanthin [55]. The sequencing of the lcy-e gene of this lutein-deficient _Chlorella vulgaris_ led to the identification of a single mutation at the position 336. The mutated Valine, instead of an Alanine, might have occurred in the active site of the lycopene \(\epsilon\)-cyclase, decreasing its activity.
# Load Testing and Analysis of a Large Span Through Simply-Supported Steel Box Arch Bridge
###### Abstract  
To evaluate the true load-bearing capacity and engineering reliability of a large span through a simply supported steel box arch bridge, a load test was conducted on the bridge. The example used in this test is the Jingchu Avenue Bridge located in Jingmen City, Hubei Province. Specifically, the static load test delineated six operational conditions, measuring parameters encompassing strain, hanger cable force, deflection, and potential cracks. The dynamic load test gauged the bridge's dynamic response and various indicators, including pulse tests, vehicle tests, jump tests, and barrier-free vehicle tests. The findings indicated that the maximum measured strain values during the static load test surpassed the calculated values; nonetheless, the verification factors and relative residual strains adhered to the code requirements, and no cracks were detected. The dynamic load test unveiled that the measured frequency values exceeded the theoretical ones, the damping ratios were within the normal range, and the measured impact coefficients were lower than the values stipulated in the code, all of which were in conformance with the code requirements. The data obtained from this experiment can be utilized to refine the long-term maintenance plan for the bridge, especially as it holds considerable value for structural health monitoring and aging assessment.  
steel box arch bridge; tied arch bridge; finite element method; static load test; dynamic load test +
Footnote †: journal: applied sciences
## 3 Mesh Stiffness of Internal Spur Gears with Tooth Wear
### Finite Element Verification  
The finite element method (FEM) is a successful strategy for evaluating the mesh stiffness, which has been widely employed in verification of the mesh stiffness of gears with defects [28; 29; 30; 31; 32]. To validate the calculated multi-tooth mesh stiffness with non-uniform wear, a finite element model using the same gear pair in Section 3.2 is established in ANSYS for result comparison and model verification. First, the three-dimensional gear models of the ring-planet gear pairs are established. Then, element type Solid185 is used to mesh the gears. The element shape is mapped hexahedral and the meshing teeth are further refined, as shown in Figure 18, to improve the precision of simulation. The shaft centers \(O_{1}\) and \(O_{2}\) are defined by Mass21 element and all nodes on the hub hole are fixed on corresponding shaft centers. Driving gear shaft center \(O_{1}\) only has the DOF of rotation around the Z-axis and the torque \(T\) is applied to the reference node \(O_{1}\) of the driving gear. Moreover, all DOFs of \(O_{2}\) are constrained. In addition, a surface-to-surface contact pair (Conta174 and Targe170) is used in the finite element modeling process. Solving the finite element model, the mesh stiffness can be calculated by the following equation:  
\[K=\frac{T}{R_{b1}^{2}\times\theta} \tag{61}\]  
where \(\theta\) represents the angular displacement at the node \(O_{1}\), and \(R_{b1}\) is the base circle radius of the driving gear.  
Figure 17: Multi-tooth mesh stiffness with non-uniform wear.  
After finite element simulation, the multi-tooth mesh stiffness is evaluated. Figure 19 compares the mesh stiffness between the proposed method and the finite element simulation, and the wear degree is Wear03. It demonstrates that the meshing ranges of the two in the single-tooth-pair and the double-tooth-pair mesh period match well. The maximum error is 6.4% in the double-tooth meshing range and 3.2% in the single-tooth meshing range for the prefect pair, and for the worn gear pair (Wear03), the maximum error is 9.8% in the double-tooth meshing range and 4.6% in the single-tooth meshing range, and the amplitude of mesh stiffness is slightly different. Therefore, the effectiveness of the proposed method for the calculation of internal spur gear mesh stiffness with tooth wear is validated, and the analytical model of internal spur gears with non-uniform tooth wear is verified indirectly.