esa-sceva/satcom-chunk-collection huggingface dataset:
# One-Dimensional Fluid-Structure Interaction Models in Presurized Fluid-Filled Pipes: A Review
## 4 Engineering Applications
### Accidents and Post-Accident Analyses  
FSI may generate overpressures higher than that predicted by [PERSON]'s formula and not only caused by water-hammer waves, but also by turbulence-induced vibrations, cavitation-induced vibrations or vortex shedding with lock-in. These phenomena are poorly understood [151] and are rarely taken explicitly into consideration in engineering designs, leading to accidents and service disruptions of important infrastructure with large social relevance e.g., industrial compounds, water and wastewater treatment plants, thermal plants, nuclear power plants, hydropower plants).  
A number of the most serious accidents due to water-hammer in pressure conduits until WWII were reviewed in [189]. Many of the failures described were related to vibration, resonance and auto-oscillation [190]. Table 4 summarizes a selection of accidents caused by strong hydraulic transients found in the literature, noting that the majority of incidents and accidents remains 'unpublished'.  
Normally, accidents in hydraulic facilities are associated not only to a single phenomenon but to a sequence of events that make the system collapse. Although not all the accidents listed in Table 4 were caused directly by FSI, in many cases, FSI is involved in this sequence of events and its understanding is crucial in post-accident analyses, such as reported in [126; 152; 191; 192]. Water-hammer related accidents in nuclear power plants was investigated by [193], where water-hammer waves compress flammable gasses to their autoignition temperatures in piping systems. In this paper, several examples of incidents and accidents are analysed enhancing the understanding of nuclear power plant explosions.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Location** & **Facility** & **Description and Citations** \\ \hline Oigawa, Japan & Penstock & A water–hammer wave, caused by a fast valve-closure, split the penstock open and produced the pipe collapse upstream [194]. \\ \hline Big Creek, U.S.A. & Penstock & Burst turbine inlet valve caused by a fast closure [195]. \\ \hline Azambuja, Portugal & Pump station & Collapse of water column separation causing the burst of the pump casing [80]. \\ \hline Lütschinen, Switzerland & Penstock & Penstock failure during draining due to the buckling produced by a frozen vent at the upstream end [80]. \\ \hline Arequipa, Peru & Penstock & The clogging of the control system of a valve resulted in buckling and the failure of the welding seams of the penstock due to fatigue [80]. \\ \hline \hline \end{tabular}
\end{table}
Table 4: Selection of historical accidents in pressurized pipe systems mentioned in the literature.
# Mixed-Integer-Based Path and Morphing Planning for a Tensegrity Drone
## 4 Configurations Dataset
### Configuration Vector  
To represent a configuration in a compact manner, a configuration vector \(\mathbf{r}\) describing the positions of all nodes of the structure is introduced:  
\[\mathbf{r}=\begin{bmatrix}\mathbf{r}_{1}^{\top}&...&\mathbf{r}_{n}^{\top} \end{bmatrix}^{\top}, \tag{7}\]  
where \(n\) is the number of nodes in the structure. To retrieve the position of an individual node from the configuration vector, indexing matrices \(\mathbf{P}_{i}\) are used:  
\[\mathbf{r}_{i}=\mathbf{P}_{i}\mathbf{r} \tag{8}\]\[\begin{cases}\mathbf{P}_{1}=\begin{bmatrix}\mathbf{I}_{3\times 3}&\mathbf{0}_{3 \times 3}&...&\mathbf{0}_{3\times 3}\\ \mathbf{P}_{2}=\begin{bmatrix}\mathbf{0}_{3\times 3}&\mathbf{I}_{3\times 3}&...& \mathbf{0}_{3\times 3}\end{bmatrix}\\...\\ \mathbf{P}_{n}=\begin{bmatrix}\mathbf{0}_{3\times 3}&\mathbf{0}_{3\times 3}&...& \mathbf{I}_{3\times 3}\end{bmatrix}\end{cases} \tag{9}\]  
A number of different configuration vectors form a configuration dataset. Different configurations in the dataset are numbered with upper scripts, with \(q\)-th configuration in the dataset represented as \(\mathbf{r}^{q}\). The total number of configurations in a dataset is denoted as \(Q\).  
With that, the test whether or not a configuration \(\mathbf{r}^{q}\) lies inside a polytope \(\mathcal{A}_{t}\) takes the form:  
\[\mathbf{A}_{t}\mathbf{P}_{i}\mathbf{r}^{q}\leq\mathbf{b}_{t},\;\forall i \tag{10}\]  
Conditions of static equilibrium can be re-formulated in a similar manner. Note that in practical development, the indexing can be implemented by reshaping the vector \(\mathbf{r}^{q}\) into a \(3\times n\) array, which can be indexed using standard matrix indexing operators; this negates the need for matrix multiplication in (8) and thus should be done when possible.
## 2 Materials and Methods
### Study Design  
This was a three-period, cross-over, acute interventional study with a one-week washout period between treatments. The participants were randomly assigned to the C: F1:F2 group (Control: Functional 1: Functional 2), the F1:C:F2 group (Functional 1: Control: Functional 2), or the F2:F1:C group (Functional 2: Functional 1: Control). Participants in the C:F1:F2 group received the control meal on the first visit, functional meal 1 on the second visit, and functional meal 2 on the third visit; those in the F1:C:F2 group received functional meal 1 on the first visit, the control meal on the second visit, and functional meal 2 on the third visit; and those in the F2:F1:C group received functional meal 2 on the first visit, functional meal 1 on the second visit, and the control meal on the third visit. The study design is illustrated in Figure 1.  
Participants were recruited by the Human Nutrition Unit on three separate days (control meal, functional meal 1, functional meal 2) at 09:00 a.m., following a 12 h overnight  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Control Meal** & **Functional Meal 1** & **Functional Meal 2** \\ \hline Energy (kcal) & 599.90 & 597.66 & 580.77 \\ Fat (total, g) & 33.06 & 33.20 & 32.48 \\ Carbohydrates (g) & 63.55 & 61.51 & 60.89 \\ Sugars (total, g) & 6.27 & 6.31 & 5.73 \\ Dietary fiber (g) & 3.18 & 4.52 & 5.64 \\ Protein (g) & 11.21 & 11.29 & 10.62 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Nutritional composition of the meals.  
fast. They filled out a short recall questionnaire referring to the meals consumed over the last 24 h and a fasting blood sample was collected from the forearm vein (referred to as 0 min-baseline). Participants then consumed 165 g of either the control or the functional meals, depending on their group allocation, along with one cup of water (250 mL) within 15 min. Blood samples (10 mL) were drawn at 30, 90, and 180 min following meal consumption.  
Ethylenediaminetetraacetic acid (EDTA)- and citric acid-treated tubes were used for plasma separation and heparinized tubes for serum separation. Plasma and serum samples were separated by centrifugation for 15 and 10 min, respectively, at 3500\(\times\)\(g\) in a clinical centrifuge after blood sample collection. Aliquots of plasma and serum samples were stored at \(-\)40 \({}^{\circ}\)C until the analysis.
On the other hand, the total number of errors is not suited to estimate small changes of the read voltages, which occur for different pages from the same block. To cope with this issue, we propose a second approach which is a gradual channel estimation method. Like the approach based on Berger codes, we exploit the asymmetric error probabilities by counting the one-to-zero and zero-to-one errors independently. Based on the asymmetry, we apply a tracking method to adjust the read references from page to page. We use information-theoretic measures to investigate the relation between the optimal read references and the statistical information obtained by counting errors. Both methods are investigated utilizing the mutual information between the optimal read voltage and the measured error values. Mutual information is also used to determine the parameters for the adaption algorithms. Numerical results based on flash measurements demonstrate that both methods can significantly reduce the BER of NAND flash memories and achieve nearly optimal performance. This publication is organized as follows. In Section 2, we review some basic properties of flash memories and introduce a model for the threshold distributions. In Section 3, we present measurement results that motivate the proposed adaption methods and describe the principal approach. Moreover, we introduce the information-theoretical measures that are used to analyze and optimize the threshold calibration. The calibration of the read reference voltages is detailed in Section 4, and the gradual estimation method in Section 5. Finally, we conclude our work in Section 6.
## 5 Conclusions  
In this paper, temperature-aware routing protocols in WBSNs have been reviewed. The pros and cons of each routing protocol have been analyzed. We have also discussed some perspectives on how a routing protocol in WBSNs should be designed. Then, the challenging open issues in designing a routing protocol for WBSNs were discussed.  
WBSNs have the potential to improve human life quality, especially for elderly people. It is expected that in the future, using WBSNs, the health of people can be continuously monitored and ambulatory without requiring them to go to healthcare facilities. However, the implementation of sensor nodes on the human body is not as simple as it seems. Many factors must be considered, which relate closely to human health, such as tissue heating and radiation problems caused by the nodes. Nevertheless, many studies are taking place to achieve the best way possible to implement WBSNs on the human body. Studies on the design of sensor nodes, MAC protocols and routing protocols are currently under way.  
We believe that a routing protocol in WBSNs must first prioritize the reduction of bioeffects as the utmost objective. The nodes are placed really close and even inside the human body. This means that human safety becomes the main concern. After the human safety concern is achieved, the other objectives, such as delay, reliability, energy efficiency or QoS, can be taken into account.
## 4 Discussion
### Contributions of Environmental Variables to GPP Estimation  
Figure 8 demonstrates the percentage distribution of the eight driving environmental factors of the GPP estimation model contributing to the GPP estimation on the Qinghai-Tibet Plateau. The results indicated that NDVI has the largest impact on GPP estimation with a share of 21.8%, followed by temperature (20.3%), and atmospheric CO\({}_{2}\) concentration (18.8%). Among the eight environmental factors, precipitation and irradiance had the weakest contribution to the GPP estimation, both with a share of 4.7%. NDVI serves as a valuable indicator of green plant vitality and exhibits a strong correlation with ecosystem carbon uptake; thus, there is a significant sensitivity of NDVI to GPP estimation. Additionally, temperature plays a crucial role in photosynthesis by modulating the activity of enzymatic processes, thus impacting vegetation's carbon sequestration capacity. Moreover, the considerable sensitivity of GPP to atmospheric CO\({}_{2}\) concentration aligns with existing studies highlighting the fertilization effect of CO\({}_{2}\) on vegetation. Given these factors, introducing atmospheric CO\({}_{2}\) concentration in GPP estimation is a rational approach to enhance the accuracy of GPP estimation.  
Figure 7: Trend patterns of GPP estimated from the improved GPP estimation model during 2003–2018 on the Qinghai–Tibet Plateau. Trend of annual cumulative GPP (**a**), proportion of GPP change trend under eco-geographical zoning (**b**) and the distribution of inter-annual coefficients of variation in GPP (**c**).
# Interference Mitigation for Metro Train Location Based on Karhunen-Loeve Decomposition
###### Abstract  
We propose a communication positioning integrated signal (CPIS) to achieve sub-meter positioning of metro trains. However, the electromagnetic environment of metro trains is complex, and the interference of radio frequency signals will reduce the accuracy of train positioning and affect the safe operation of trains. In this paper, an interference mitigation method for metro train positioning based on Karhunen-Loeve decomposition is proposed. The signal is orthogonally expanded, and the eigenvalues of the interference signal are obtained according to the distribution characteristics of the eigenvalues. The reconstructed interference signal is then subtracted from the received signal to achieve interference suppression. The experimental results demonstrate the effectiveness of the method in interference scenarios.  
train positioning; communication positioning integrated signal; interference mitigation; Karhunen-Loeve decomposition +
Footnote †: journal:  
_Applied_  
Footnote †: journal:
# Numerical Simulation of Non-Linear Models of Reaction--Diffusion for a DGT Sensor
## 2 The Model
### Diffusive Gradients in Thin Films (DGT)  
The analytical technique known as DGT consists of simple devices developed for the in-situ measurement of bioavailability of heavy metals in soils and aquatic environments [6; 7].  
The DGT sensors are made up of three layers (see Figure 2): a hydrogel layer impregnated with a complexing material (resin layer), a hydrogel used as a diffusion layer, and a membrane filter. These three layers are placed in a plastic holder device [7].  
DGT sensors were designed to emulate the metal absorption process in aqueous media. When a DGT is deployed in a sampling solution, where all species are in equilibrium, the metal species diffuse through the diffusive gel (where they might associate or dissociate), toward the resin layer where they accumulate as metal resin complexes.  
Figure 2: Representative scheme of a Diffusive Gradients in Thin Films (DGT) sensor. The diagram on the left represents a 3-D view and on the right a cross-section view, when the device is assembled.  
As long as the resin does not get saturated, a concentration gradient is maintained in the diffusion layer. Very soon, (quasi) steady-state conditions are reached, so the initial transient can be neglected to obtain easy approximate analytical expressions. [PERSON]'s first law of diffusion describes the flux (\(J_{i}\)) for each species \(i\),  
\[J_{i}=D_{i}\frac{\mathrm{d}c_{i}}{\mathrm{d}x}\, \tag{5}\]  
where \(D_{i}\) is the diffusion coefficient, \(c_{i}\) is the concentration and \(\mathrm{d}c_{i}/\mathrm{d}x\) is the gradient of spatial concentration of the \(i\) species.  
After the DGT has been exposed to the solution, it must be opened and the analytes eluted from the resin to be quantified.  
When only metal is present in the solution, the accumulated mass in DGT is related to the solution concentration through a simple equation that neglects the effects of dynamic speciation [6]:  
\[n=D\frac{c_{\mathrm{M}}^{*}}{g}At\, \tag{6}\]  
where \(n\) stands for the accumulated moles, \(D\) represents an average diffusion coefficient, \(c_{\mathrm{M}}^{*}\) is the metal concentration at the solution, \(A\) is the DGT area, \(t\) is the deployment time, and \(g\) is the diffusion layer thickness.  
In the presence of metal complexes, \(c_{\mathrm{M}}^{*}\) is replaced with \(c_{\mathrm{DGT}}^{*}\), which indicates the effective or apparent metal concentration leading to this accumulation. \(c_{\mathrm{DGT}}^{*}\) is usually called the labile metal concentration of the solution.  
The rigorous interpretation of \(c_{\mathrm{DGT}}^{*}\) in terms of the real species in solutions requires taking into account all reaction-diffusion processes involved in the system, which can only be achieved via numerical simulation.
## 3 Results and Discussion
### Stage 2
#### 3.2.6 Validation and Improvement  
The results indicated a process with greater operational control and an increase in the products' quality. However, the productivity level decreased, which represents a loss for the enterprise. While it is essential to keep implementation costs low to improve product pricing, it is crucial to have adequate capacity to meet customer demand. The team concluded that the enterprise must work together to enhance productivity, as stated by [PERSON] and [PERSON] [40].  
The results and experiences indicated that the knowledge of the managers is not sufficient for effective planning. The implementation team must pay attention to technical dimensions to improve the management's knowledge, which allows for better understanding and effective use of resources. The operational experts' participation was valuable for the adaptation of the processes based on the characteristics of the product. For this reason, the team determined that it is essential to include operational leaders in the planning stage so that the empirical knowledge of the staff and the knowledge acquired from the administrative staff complement each other. Doing this delivers improvements on resources, time, money, administration, equipment, and design method, as stated by some authors [35; 51].  
It is not easy for enterprise staff to switch to a new work system. Therefore, it is necessary to continue collaborative work and eliminate implementation barriers such as lack of concurrent job training, lack of internal experience and management support, inadequate communication, inadequate organizational structure [16], and changes in the staff's mentality [55]. Despite the inconveniences that arose in this project, the team members showed a friendlier dynamic and less resistance to the decisions made under this new way of working.  
At the end of the Reex project, production continued with a standardized product, so the DINP model's application concluded. According to the model presented, in case of having a new product implementation, the process would restart in stage 2, in \"Project choice\" step. A further sensitization of CEOs (Stage 1) would not be necessary.
# Numerical Simulation of Cavitation Erosion Aggressiveness Induced by Unsteady Cloud Cavitation
## 4 Conclusions  
In the present study, the dynamic behavior and the erosion power of unsteady cloud cavitation on a 2D hydrofoil has been investigated numerically based on an energy balance approach. The influences of the driving pressure and of the cavitation model have been discussed in detail. The numerical prediction of erosion is in agreement with the experimental measurements and with the main erosion mechanisms. Moreover, the influence of flow velocity on erosion power has been quantified. As a result, the following conclusions have been obtained:  
1. The selection of the driving pressure to estimate the power of the cavity collapse has a significant effect on the space-time distribution of the cavitation aggressiveness on the hydrofoil surface. The use of the average pressure gives more similar results to the experiment than the use of the instantaneous pressure.
2. The cavitation model influences significantly the power loaded on the hydrofoil surface both in terms of magnitude and spatial distribution along the chord. For the cases considered in the present study, the Sauer model performs better than the [PERSON] and Zwart ones.
3. Two main erosion mechanisms have been predicted that are in good agreement with experimental observations. One is induced by the high frequency of low-intensity collapses taking place at the closure region of the main sheet cavity attached to the hydrofoil surface. The other one is induced by the low frequency and high intensity collapses of the shed cloud cavities.
4. Power laws have been obtained that permit the calculation of the erosive cavitation intensity as a function of the flow velocity by taking into account the collapse efficiency and the shedding frequency. More specifically, the effective power load law grows with an exponent of 4, and the erosion aggressiveness per unit time grows with an exponent of 5.  
**Author Contributions:** Methodology, L.G., J.C. and X.E.; software, L.G.; validation, L.G., J.C. and X.E.; investigation, L.G., J.C. and X.E.; writing--original draft preparation, L.G.; writing--review and editing, L.G., O.D.L.T. and X.E.; supervision, O.D.L.T. and X.E. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The present research work was financially supported China Scholarship Council.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 6 Constraints Applied to the Optimization Algorithm
### Constraints on the Correlations among Phase Currents and among Layer Currents  
The HTS layer currents derived from Equation (6) add up to form the corresponding phase currents. Clearly, in the case of phases composed of single layers, the layer and phase currents are equal. For the line to be operated in real AC grids, it is imposed that the phase arrangement forms a balanced three-phase system. In the model, this is accomplished by checking that the relative difference between the rms value of each phase current compared with the designated rms phase value (\(I_{phase_{rms}}\)) stays below a user-defined threshold %\({}_{maxphases}\). In terms of equations, it is verified that  
\[\left|\left|\sum_{j=1}^{nl_{i}}\underline{I_{i,j}}\right|-I_{phase_{rms}} \right|/I_{phase_{rms}}\leq\%_{maxphases}\qquad for\ i=1,\ 2,\ 3 \tag{38}\]\[I_{phase_{rms}}=\frac{P}{\sqrt{3}\ V_{rms}{\rm cos}\varphi} \tag{39}\]  
Similarly, a constraint is imposed to check that the superconducting layers equally share the current of the phase where they belong. This ensures a balanced workload across layers, preventing some tapes from operating at currents nearing their critical value while others remain underloaded. To achieve this, the following constraint is imposed on each layer current:  
\[\frac{\left|\left|I_{i,j}\right|-\left|I_{i,j+1}\right|\right|}{max\left( \left|I_{i,j}\right|,\left|I_{i,j+1}\right|\right)}\leq\%_{max_{layers}}\ \ \ \ for\ i=1,2,3\ \ \ \ and\ \ \ for\ j=1,\ldots,nl_{i}-1 \tag{40}\]  
where \(\%_{max_{layers}}\) represents the maximum threshold set by the user for the relative difference between the amplitude of a given layer current and the amplitude of the other layer currents within the same phase.  
The imposition of Equations (38) and (40) places an implicit constraint on the mutual inductances of the HTS layers and therefore on the relative winding angles and radii.  
It is worth noting that in this version of OSCaR, it is no longer necessary to apply a safety criterion concerning the maximum current flowing in the HTS tapes of the optimized cable design. Indeed, this is automatically satisfied, having selected \(\sqrt{2}\left|I_{lape_{i,1}}\right|/I_{c}\left(T_{HTS_{max}},\ |B|_{i,1},\ \theta_{i,1}\right)\) as system variables and applied corresponding safety and underload limits to these terms.
## 4 Discussion
### Clinical Implications of Movement Type  
The findings underscore the importance of selecting appropriate mandibular movements for condylar guidance measurements. Protrusive movements, characterized by their consistency and reliability, are reaffirmed as the gold standard for articulator calibration. Moreover, CGA plays a pivotal role in determining occlusal morphology, as variations in CGA directly influence the morphology of posterior teeth. Studies [27; 28; 29] have shown that changes in CGA can alter cusp angles and occlusal relationships, affecting both the functionality and esthetics of restorations. Digital tools, such as the Modjaw device, offer a transformative approach to condylar guidance assessment by enabling real-time tracking of mandibular dynamics, thereby enhancing workflow efficiency and accuracy in prosthodontic treatments. These tools not only streamline clinical processes but also ensure the accurate reproduction of functional dental morphology tailored to individual patients.