esa-sceva/satcom-chunk-collection huggingface dataset:
# InSAR-CTPIM-Based 3D Deformation Prediction in Coal Mining Areas of the Baisha Reservoir, China
## 2 Methods
### InSAR-3D-CTPIM Parameter Estimation by GA-SM  
In this paper, a genetic algorithm (GA) is used to estimate the unknown parameters \(G_{2}\) and \(c\) of the model in parallel with the simplex method (SM). For each high-coherence point (\(x,y\)) on the generated \(M\) differential interferograms, \(M\) equations can be listed, and the number of InSAR observations of more than eight views can be obtained from the unknown parameters. And the InSAR phase observations are used as inputs to estimate the model parameters [30]. The fitness function of the genetic algorithm can be expressed as follows:  
\[f=\min\parallel\delta\varphi-\delta\varphi^{m}\parallel \tag{19}\]  
where \(\delta\varphi\) denotes the differential interferometric phase obtained from InSAR measurements; \(\delta\varphi^{m}\) is the differential interferometric phase calculated from the unknown parametersobtained by the GA algorithm according to Equation (18). When using the GA search, the geological parameters \(G_{1}=[m,H_{1},H_{2},\alpha,\omega,D_{1},D_{3},v_{m}\) ] can be determined according to the geological data of the mine area [31], which are regarded as known parameters, and the range of values of the model parameters \(G_{2}\) are shown in Table 1.  
The search for unknown parameters using the GA method is more dependent on the initial range of the population, and the optimal evaluation of the unknown parameters obtained may not reach the optimal value within a limited number of iterations, which may therefore lead to poor accuracy or even an incorrect output solution. The accuracy of model parameter estimation can be improved by using SM for secondary search [32]. The flow of the GA-SM algorithm is (1) GA search to obtain the initial solution of unknown parameters as the initial value of SM and construct an initial simplex form with nine solutions; (2) Calculate the value of the fitness function to determine the worst, second worst, and optimal solutions; and (3) Expand, compress, and worst-compress operations using the reflection point until the search for a solution better than the worst solution stops; perform multiple superpositions to find the optimal solution as the final evaluation of \(G_{2}\). The process of 3D-CTPIM to predict the three-dimensional deformation of the mine is shown in Figure 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Range of Values** \\ \hline \(q\) & 0–1 \\ \(d\) & 0.1–0.5 \\ \(\tan\beta\) & 1–3.8 \\ \(\theta_{0}\) & 80–90 \\ \(S_{1}\) (\(m\)) & 5–30 \\ \(S_{2}\) (\(m\)) & 5–30 \\ \(n\) & 1/7–1/2 \\ \(c\) & 6–17 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Range of model parameter values.  
Figure 3: Flow of 3D deformation prediction based on InSAR-3D-CTPIM.
## 3 Models and Design Goals
### Multicast Subsystem
#### 3.2.1 Steiner Tree  
In general, to connect \(n\) nodes, the Minimum Spanning Tree (MST) is the most commonly selected algorithm. But in networks, there are lots of factors that need to be taken into consideration, such as bandwidth, transport delay of networks and so on. Hence, Steiner Tree, a spanning tree algorithm with weights, is more suitable. The generation of Steiner Tree is thought to be computation intensive, so there are few applications in conventional multicast schemes. But as for SDN controller, it becomes feasible since the forwarding information is preloaded in network switches. In addition, the global visibility and programmability can also help to construct a better multicast tree more efficiently [21].
# Numerical Analysis on the Performance of a Heat Pump Powered by Surface Water Freezing
## 2 The Heat Pump System and Its Numerical Model
### Compressor Simulation Model  
The working process of the compressor is regarded as isentropic compression described by Equations (3)-(5). By inputting boundary conditions including evaporation temperature, condensation temperature, superheated degree, and refrigerant mass flow into the model, the compressor discharge temperature and isentropic power are obtained by simulation. During the modeling process, the following assumptions are applied:  
1. The compression process is regarded as adiabatic compression, ignoring the heat exchange between the compressor shell and the surrounding environment.
2. Regardless of the pressure loss of the refrigerant at the inlet and outlet of the compressor, the evaporator outlet pressure is equal to the compressor suction pressure, and the condenser inlet pressure is equal to the compressor discharge pressure.
3. Do not consider the friction power consumption in the compressor, the influence of lubricating oil, etc. \[\frac{P_{\mathrm{d}}}{P_{\mathrm{r}}}=\left(\frac{v_{\mathrm{r}}}{v_{\mathrm{ d}}}\right)^{k}\] (3) \[\frac{T_{\mathrm{d}}}{T_{\mathrm{r}}}=\left(\frac{P_{\mathrm{d}}}{P_{ \mathrm{r}}}\right)^{\frac{k-1}{k}}\] (4) \[w=\frac{k}{k-1}P_{\mathrm{r}}v_{\mathrm{r}}\Bigg{[}1-\left(\frac{P_{ \mathrm{d}}}{P_{\mathrm{r}}}\right)^{\frac{k-1}{k}}\Bigg{]}\] (5)
## 3 Results
### Raman Spectroscopy Measurements of CNT Web Film  
CNT web film was measured using Raman spectroscopy to directly observe the damage to the CNT web film after repeated flush mold heating experiments. As shown in Figure 6, five points of the CNT web film were designated and observed. At the P1 point, the actual contact part and the non-contact part were separated and measured into P1-1 and P1-2 points, and the Raman measurement results are shown in Figure 7. The degree of defect in the CNT web film was confirmed through the \(D/G\) peak. The sample's P0, P1-1, P1-2, P2, and P3 points with a flush of 0 mm presented \(D/G\) peaks of 0.18, 0.16, 0.18, 0.14, and 0.14 on average, respectively. Each point of the 0.1 mm sample presented \(D/G\) peaks  
Figure 5: Resistance variation of CNT web film by flush and temperature range: (**a**) 0 mm flush at 30 \({}^{\circ}\)C \(<\) T \(<\) 60 \({}^{\circ}\)C, (**b**) 0 mm flush at 130 \({}^{\circ}\)C \(<\) T, (**c**) 0.1 mm flush at 30 \({}^{\circ}\)C \(<\) T \(<\) 60 \({}^{\circ}\)C, (**d**) 0.1 mm flush at 130 \({}^{\circ}\)C \(<\) T, (**e**) 0.3 mm flush at 30 \({}^{\circ}\)C \(<\) T \(<\) 60 \({}^{\circ}\)C, (**f**) 0.3 mm flush at 130 \({}^{\circ}\)C \(<\) T.  
of 0.18, 0.18, 0.21, 0.35, and 0.39, respectively. Each point of the 0.3 mm sample presented \(D/G\) peaks of 0.23, 0.26, 0.41, 0.25, and 0.31. When there was no flush (0 mm), similar peak values were presented at all points, and no defects appeared. However, the defect caused by the flush showed a maximum peak of 0.41 at the P1-2 point of 0.3 mm. Hence, a non-contact overheating defect occurs when the flush is over 0.3 mm.
# QoS-Aware Flexible Handover Management in Software-Defined Mobile Networks
## 3 QoS-Based Flexible Handover Scheme in SDN
### QoS-Based Flow Classification  
As many works have shown, the SDN architecture has an inherent scalability problem in both the control [45] and forwarding plane [42; 43; 46]. Specifically, due to the centralized and forwarding/control plane separation architecture, the control plane fails to process all the input requests from the forwarding plane as the network size increases. In addition, current forwarding nodes have a resource limit for flow-state awareness [47]. This means that the classification of flows to provide flexible handover procedures is required considering the scalability issue rather than per-flow granularity.  
In order to classify the flows, this paper considers the QoS requirements of flows in terms of delay and loss tolerance for handover performance. Based on our previous work [14], four kinds of classes are defined according to the QoS categories shown in Table 1.  
Class 1 includes delay and loss-tolerant flows which can tolerate an end-to-end delay of a few seconds and a packet loss ratio of over 3%, such as best effort service flows. File transfer protocol (FTP) and email services flows are involved in Class 2, which are sensitive to packet loss while a long handover delay can be tolerable compared to other classes. This means that a loss ratio of 0% is strictly required for Class 2. Class 3 contains both delay and loss-intolerant flows, which means that it has the strictest QoS requirements during handover compared to other classes. Interactive real-time games and video telephony as well as alert commands and control flows can be included in Class 3; at least, very short delays under 150-400 ms and packet losses of at most 0-2% percent should be guaranteed during handover for Class 3. On the other hand, Class 4 has a delay-sensitive property rather than packet loss during handover. For example, voice over IP (VoIP) and one-way audio/video streaming services flows can be included in Class 4. This class requires a very short handover delay, as with Class 3, but an about 3% packet loss ratio can be tolerable. A more detailed explanation of the properties and examples of each class are described in our previous work [14].
# Wideband Versatile Receiver for CubeSat Microwave Front-Ends
###### Abstract  
One of the main features of CubeSats is represented by their extreme versatility, e.g., maintaining the same overall structure for different purposes. This requires high technological flexibility achievable in a cost-effective way while maintaining compact sizes. In this contribution, a microwave receiver specifically designed for CubeSat applications is proposed. Due to the wide input operating bandwidth, i.e., 2 GHz-18 GHz, it can be exploited for different purposes, e.g., satellite communication, radars, and electronic warfare systems. This is beneficial for CubeSat systems, whereby the possibility to share the same front-end circuit for different purposes is a key feature in reducing the overall size and weight. The downconverter was designed to minimize the spurious contributions at low frequency by taking advantage, at the same time, of commercial off-the-shelf components due to their cost-effectiveness. The idea behind this work is to add flexibility to the CubeSat communication systems in order to be reusable in different contexts. This feature enables new applications but also provides the largest bandwidth if required from the ground system. An accurate experimental characterization was performed to validate the downconverter performance with the aim of allowing easy system integration for the new frontier of CubeSat technologies. This paves the way for the most effective implementation of the Internet of Things (IoT), machine-to-machine (M2M) communications, and smart-everything services.  
CubeSat; down converter; microwaves receivers; satellite communications; space applications; spurious reduction; tuners; internet of things; wide bandwidth
## 3 Preprocessing Process
### Optimal Image Data Generation  
When the optimal value is obtained, the following image generation process is performed: (1) when the original image is input, non-uniform pixel values are evenly adjusted and Gaussian blur processing is performed to mitigate noise; (2) after applying the contrast enhancement technique (CLAHE) to the L (luminosity) channel of the lab channel using the optimal 'cliplimit' value, it is converted to the RGB channel again; (3) the brightness is increased according to the optimal '\(\alpha\)' value for the contrast-enhanced image; (4) the saturation arithmetic is applied to the contrast-enhanced image to maintain image quality by preventing the brightness or color values of the image data from changing too much;  
Figure 4: Example image showing absolute brightness difference and cliplimit optimal value.  
Figure 5: Example image of sharpness and ‘\(\alpha\)’ (additional brightness) optimal value obtained via edge-detection.  
(5) the image is overlaid with the saturation arithmetic by applying alpha blending to the original image.  
Alpha blending is a display method that mixes the background RGB value and the RGB value above it by assigning a new value called 'Alpha' to the computer's color expression value 'RGB' for a light effect when another image is overlaid on top of the image. Alpha blending is applied because in the case of an image to which a simple contrast enhancement technique is applied, pixels are often damaged to improve contrast, resulting in noise areas and degrading data quality.  
In this paper, alpha blending was applied to the original image and the contrast-enhanced image by specifying a ratio of 0.5, and through Figure 6 it can be seen with the naked eye that the degree of image damage is reduced when alpha blending is applied.  
The core goal of the algorithm proposed in this paper is to improve problem areas caused by light or lighting conditions and improve quality by preserving similarity to the original image while minimizing pixel damage. This has the effect of overcoming the disadvantages of existing image contrast enhancement technologies, which require the setting of appropriate hyperparameters because the contrast of input image data varies depending on the lighting environment.  
Figure 7 shows the resulting image at each stage of optimal image creation. As the creation stage progresses, the changes between the input and the results can be checked.  
Figure 6: Original image, alpha blending image, and contrast enhancement image.
# Merging Discrete Morse Vector Fields: A Case of Stubborn Geometric Parallelization
## 1 Introduction  
Discrete Morse theory [1] is a fairly new and powerful tool, created by [PERSON] in the 1990s, that has many applications in many fields. One important application of discrete Morse theory is streamlining homology calculations. It has been a very useful tool in topological data analysis, for example, in computing persistent homology.  
More specifically, there are algorithms that take both 2D and 3D digital grayscale images and create discrete Morse functions from their grayscale function values defined on pixels or voxels. These images give rise to cubical complexes, where the voxels or pixels are the 0-cells of the complex. One algorithm, ProcessLowerStars from [2], takes all of the cells in the cubical complex and puts them either singly in the set of critical cells, \(C\), or as a face-coface (of codimension 1) pair in the discrete vector field, \(V\). The set of critical cells and the discrete vector field are the defining features of a discrete Morse function, and in turn the discrete Morse complex. In discrete Morse theory, the discrete vector field determines the maps in the simplified chain complex of the Morse complex that is created from the critical cells of the discrete Morse function.  
Given a general algorithm \(a\) applied to cells in a regular CW complex \(K\), which takes data from \(k\)-neighborhoods of cells in \(K\) and uses those data to either pair cells together in a list, which is to become the discrete vector field \(V\), or places the cells singly in the list of critical cells \(C\). The basic question we address is, how do we merge the vector fields in the elementary situation where \(K=U\cup W\), and \(U\), \(W\) are two subcomplexes to which \(a\) is applied individually to obtain the discrete vector fields \(V(U)\) and \(V(W)\)? The goal is to reconstruct the correct vector field \(V(K)\) that we would obtain on all of \(K\). It turns out this is not straightforward in that \(V(K)\
eq V(U)\cup V(W)\), and so \(a\), is not embarrassingly parallelizable. Being able to perform such a computation will involve looking at wheremistakes occur in naively merging the vector fields together as in the right-hand side of the formula (or, in general, related vector fields) and \"crossing out\" those mistakes.  
What do we mean by an algorithm being embarrassingly parallelizable? One can take an example with operators in calculus to illustrate the difference between embarrassingly and stubbornly parallelizable algorithms. Differentiation is embarrassingly parallelizable with respect to addition and subtraction, i.e., \((f(x)\  
dimension. A sufficient partition scheme in a product of trees, therefore, provides a generic template to approach most common regular cellular complexes. On the other hand, we find that a tree is an example that distinguishes the advantages of each of the two main parallelization results of the paper.  
We finish with a collection of remarks on applications of the results, the literature, and directions for future work in Sections 6 and 7.
# Forward Link Optimization for the Design of VHTS Satellite Networks
## 3 Fundamentals and Analytical Models
### Spot Beam Model and Link Budget Calculation
The downlink _CNR_ is calculated as:  
\[\textit{CNR}(\textit{lat},\textit{lon})=\textit{EIRP}(\textit{lat},\textit{lon })+\frac{G}{T}(\textit{lat},\textit{lon})-FSL(\textit{lat},\textit{lon})-k-A_ {T}(\textit{lat},\textit{lon},\textit{P})-10 log_{10}(BW_{b}) \tag{10}\]  
where  
* \(\textit{EIRP}(\textit{lat},\textit{lon})\) is the EIRP of the satellite beam in the (lat, lon) grid,
* \(BW_{b}\) is the available bandwidth to each beam in Hz,
* \(\textit{CNR}(\textit{lat},\textit{lon})\) is the carrier to noise ratio in the geographical position in dB,
* \(\frac{G}{T}(\textit{lat},\textit{lon})\) is the figure of merit of the user terminal in the geographical position (lat, lon) in dB/K,
* \(FSL(\textit{lat},\textit{lon})\) represents the free-space losses in geographical position in dB,
* \(k\) is Boltzmann constant (\(-228.6\) dBW/(K\(\cdot\)Hz)), and
* \(A_{T}\) (_lat_,_lon_,_P_) is the total atmospheric attenuation (in dB) for a link availability \(P\) calculated using an ITU recommendation [26].
## 2 Establishment of Model and Experiment
### Experimental
#### 2.1.2 Data Verification  
The heating capacity of the two systems was calculated according to the heat exchange capacity of the water-heating side using Equation (1), the cooling capacity of the two systems was calculated according the heat exchange capacity of the chilled water using Equation (2), and the heating COP of the system was obtained by Equation (4). The errors between the heating capacity for the water-heating side and Equation (3) were within \(\pm 8\%\), indicating that the tested system exhibited effective heat balance.  
Uncertainty analysis was performed to verify the measured data of the heating capacity and COP using Equation (5) [31], where E represents the targeted capacity or COP, Y\({}_{i}\) represents the uncertainty of its affecting factors, and N is the number of affecting factors. Based on that, the relative uncertainties of the heating capacity and COP of the two systems are shown in Table 5.  
\[Q_{h}=m_{h\text{-}w}C_{p}(T_{h\text{-}w\text{-}o}-T_{h\text{-}w\text{-}i}) \tag{1}\]  
\[Q_{e}=m_{e\text{-}w}C_{p}(T_{e\text{-}w\text{-}i}-T_{e\text{-}w\text{-}o}) \tag{2}\]  
\[Q_{h}=m_{e\text{-}w}C_{p}(T_{e\text{-}w\text{-}i}-T_{e\text{-}w\text{-}o})+W \tag{3}\]  
\[COP_{h}=Q_{h}/W \tag{4}\]  
\[\frac{\delta E}{E}=\sqrt{\left(\sum_{i=1}^{N}\left(\frac{\delta Y_{i}^{ \prime}}{Y_{i}}\right)^{2}\right)} \tag{5}\]  
where \(Q_{h}\) is the heating capacity (kW), \(Q_{e}\) is the cooling capacity (kW), \(m_{h\text{-}w}\) is the mass flow of heating water (kg/s), \(m_{e\text{-}w}\) is the mass flow of chilled water (kg/s), \(T_{h\text{-}w\text{-}o}\) is the outlet temperature of heating water (K), \(T_{h\text{-}w\text{-}i}\) is the inlet temperature of heating water (K), \(T_{e\text{-}w\text{-}i}\) is the inlet temperature of chilled water (K), \(T_{e\text{-}w\text{-}o}\) is the outlet temperature of chilled water (K), \(W\) is the consumption of the compressor (kW), \(C_{p}\) is the specific heat of water (kJ/(kg\text{-}K)), and \(COP\) is the coefficient of performance.
# A Snapshot on the Occurrence and Risk Assessment of Organic Pollutants in an Urban River
###### Abstract
After an extensive review, the only existing approaches, scanning for CECs in Portuguese freshwaters, were the references [36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46] in superficial waters and WWTP. PAHs and PCBs have been mainly monitored in estuarine and marine ecosystems [47; 48; 49; 50], except for the evaluation of the presence of PCBs performed by [51] in a small intermittent urban stream. To our knowledge, this study is the first approach to CECs, PAHs, and PCB(s) carried out in the upper Portuguese part of the Douro basin (NE Portugal). For this purpose, the Fervenca river was used as an example of a stressed urban river in a low-density region. Therefore, the objectives of this exploratory study were: (a) to determine the occurrence and environmental risk of paracetamol, ibuprofen, diclofenac, clofibric acid, carbamazepine, ofloxacin, caffeine, HHCB, AHTN, and BPA both in water and sediment lixiviates; (b) to estimate the occurrence and environmental risk of PCBs (IPCBs: 28; 52; 101; 138; 153, and 180; DLPCBs: 77; 81; 105; 114; 118; 123; 126; 156; 157; 167; 169; 189) and PAHs in sediments; (c) to assess in a relatively simple and straightforward form the environmental risk posed by the different studied substances, following a procedure that is easily adapted for other substances not studied here. Additionally, the obtained results were compared with dispersed studies on these contaminants, carried out previously in other European countries, and gathered in Tables S1 and S2. We would like to stress the relevance of this preliminary information for river managers and decision-makers. A four-color alert system is included to inform about the level of risk associated with the amount of each CEC, PAH, and PCB.
# RBC Storage Lesion Studies in Humans and Experimental Models of Shock
## 8 Effects of Washing Older versus Fresher Stored RBCs Prior to Transfusion  
We next conducted a blinded, randomized controlled study of washing RBCs prior to transfusion in our canine model of septic shock [15]. We hypothesized that the stress of washing older units of blood would cause hemolysis and remove older fragile RBCs prior to transfusion and thereby would improve clinical outcomes by preventing increases of CFH and iron. However, washing fresher units would affect the outcome less. Two-year-old purpose-bred beagles (n = 24) with _S. aureus_ pneumonia induced septic shock were exchange-transfused with either fresher (7-day-old) or longer stored (42-day-old) universal donor canine blood (80 mL/kg in 4 divided doses) that had been either washed (commercially employed Haemonetics blood cell processor with commonly used washing procedure, Haemonetics Corp., Braintree, MA, USA) or unwashed. Depending on if the blood was fresh or old, washing RBCs before transfusion had a significantly different and opposite effect on canine survival, organ injury, and plasma iron and CFH levels (all \(p<0.05\) for interactions). Washing older units of blood resulted in improved survival rates, hemodynamic profiles, lung injury, cardiac performance, and liver function as well as reduced levels of iron, possibly by lysing and washing away older damaged cells and CFH and iron in the supernatant. In contrast, when fresh blood was washed, these clinical parameters worsened and levels of CFH increased. Our data suggest that the act of washing fresh blood induces membrane damage to the RBC and, in a setting of established infection, washed RBCs are more likely to lyse in vivo and release iron and CFH which results in worsening outcomes. This finding indicates that fresh RBCs should not be washed routinely, especially in the setting of established infection. Together, these findings suggest that in critically ill subjects with infection, transfusion of fresh blood is preferable to older stored cells as it results in less hemolysis, CFH and iron release, and has proved less toxic than transfusion of older blood. Furthermore, if older blood must be transfused during established infection, washing helps by removing older, more fragile cells mitigating the elevations in plasma circulating iron, improving survival, and decreasing multiple end organ injuries.