esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### CFD Analysis  
CFD models were successfully implemented and all the solutions reached convergence. Velocity patterns were analyzed to qualitatively and quantitatively assess the perfusion of the LAAs along the cardiac cycle. Figure 6 shows the velocity streamlines for all the models in two selected time instants, i.e., at velocity peak (t\({}_{1}\), when the valve is open) and at the end of the cardiac cycle (t\({}_{2}\), when the valve is closed and the fluid is circulating within the LA). Details of the velocity fields for all the four LAA shapes are showed in Figure 7, in which three particular time instants are depicted, i.e., before the velocity peak (t\({}_{0}\)), at the velocity peak (t\({}_{1}\)) and after (t\({}_{2}\)). Volume cells with a velocity values below 0.001 cm/s were highlighted in Figure 8, reporting also the percentage volume with extremely low velocity for each LAA shape. A quantitative analysis of the velocity trend along the LAA, from the ostium of the distal tip, was performed (Figure 9).  
Figure 5: Boxplots of volume (**a**), ostium area (**b**), centerline length (**c**) and tortuosity (**d**) measurements of LAA morphologies over a cardiac cycle.  
Figure 6: Velocity streamlines of each LAA morphology, i.e., CW (**a**,**e**), CF (**b**,**f**), WS (**c**,**g**) and CS (**d**,**h**) with the respective LA at t\({}_{1}\) time (**a**–**d**) and at t\({}_{2}\) time (**e**–**h**) of the cardiac cycle in terms of MV velocity profile (**i**).  
Figure 8: Velocity field for a specific plane with overlapped volume cells presenting a velocity value below 0.001 cm/s for CW (**a**), CF (**b**), WS (**c**) and CS shape (**d**).  
Figure 9: Flow velocity at three different planes: ostium section (\(S_{1}\)), mid section (\(S_{2}\)) and distal tip section (\(S_{3}\)).
## 3 Life-Cycle Costs (LCC)  
The life cycle of a structure is always referred to as its full life span covering its construction, operation, and maintenance stages. The life-cycle costs of a high-speed railway tunnel represent all the economic resources required over the design life of the project. These resources include the initial capital costs needed for the construction of necessary facilities and structures, subsequent operation, and maintenance costs used to improve and maintain the project services throughout the overall design life of the project. Among these costs, capital costs are confined to the initial years of construction while operation and maintenance costs are incurred throughout the overall life of the design. These economic resources are usually quantified and discounted back to the present value. This is referred to as the net present value (NPV). This section will discuss the capital costs and operation and maintenance costs separately. Then, the LCC analysis of the tunnel will be conducted. The construction of the tunnel consists of 6 steps which are excavation, muck disposal, primary support installation, ground control measures, ventilation and air conditioning, and permanent lining installation. Most of steps are considered in the construction stage except ventilation and air conditioning which not included in this study.  
The Xikema No.1 Tunnel is about 1 km northeast of Changqing. The location of the tunnel compared to the line is at KM420 + 395 to KM423 + 207. The length of the tunnel is 2.812 km. The range of the slope is 5.5% to 15%. The maximum design vertical curve is 30,000 m while the horizontal curve is 7000 m. The width of the tunnel is 8.2 m.
# Multibyte Electromagnetic Analysis Based on Particle Swarm Optimization Algorithm
## 4 PSO-Correlation Electromagnetic Analysis (PSO-CEMA)
In our method, for selected particles with lower fitness values, they will change their current positions to other positions randomly to have better fitness values:  
\[X=X*P_{m} \tag{12}\]  
The length of cryptographic algorithms' key is usually very long to ensure the safety. In other words, there are a large number of points in multidimensional coordinates. For example, AES is a symmetric encryption algorithm that can process data using cipher keys with 16 bytes. The range of one byte's value is from 0 to 255, so there will be 25616 points in the multidimensional coordinate. It may need large numbers of initial particles to find the right key in the multidimensional coordinate or it may need large numbers of iterations to get the right key. For simplicity, PSO is fixed a directional route to optimize the solution. Taking three-dimensional coordinates, for example, shown in Figure 3, red dots represent the initial particles' positions, the yellow triangle represents the right key's position, K(\(xbest\), \(ybest\), \(zbest\)). The particle swarm's directional route is the \(y\)-axis, \(x\)-axis, and the \(z\)-axis. In other words, particles will firstly find the best point K(0,\(ybest\),0) in K(0,\(y\),0), then find the best point K(\(xbest\), \(ybest\),0) in K(\(x,ybest\),0) and then find the right point K(\(xbest\), \(ybest\), \(zbest\)) in K(\(xbest\), \(ybest\), \(z\)) (\(x,y,z\) are random variables range from 0 to 255). Thus, Hamming distance function in Equation (8) is revised:  
\[AW=\sum_{j=1}^{t}HW(h_{j}\oplus f(h_{j},x_{j}))+\sum_{j=t+1}^{m}HW_{default} \tag{13}\]  
where \(t\) indicates the current dimension number in a directional route, and \(m\) is the number of secret key byte. Here, \(HW_{default}\) is set to 0, and it also can be set to other values. PSO-CEMA can be summarized as Algorithm 1. Figure 3: The basic schematic diagram of the Particle Swarm Optimization—Correlation Electromagnetic Analysis. ```
Input: max iteration \(max_{iter}\), number of initial particle \(N_{p}\),mutation probability\(P_{m}\); Output: the best point \(key\_points\) = Initialization(\(N_{p}\)) // initialize all particles Fitness(\(key\_points\)) //compute every particle's fitness (\(pbest\), \(gbest\)) = PickFitness(\(key\_points\)) //pick the best position \(pbest\) obtained so far and the global best position \(gbest\) of the whole swarm \(iter\) = 0 key_right = 0 whileiter \(<max\_iter\)and\(key\_right\) = 0 do Update(\(key\_points\)) //update velocities and positions according to Equation (10) and Equation (3) Mutation(\(key\_points\),\(P_{m}\)) //mutation operator Fitness(\(key\_points\)) //update fitness (\(pbest\), \(gbest\)) = PickFitness(\(key\_points\)) //pick the the best position \(pbest\) and the global best position \(gbest\) of the whole swarm ifVertification(\(gbest\)) =truethen key_right = 1 \(best\_point\) = \(gbest\)  end if return\(best\_point\)
```  
**Algorithm 1**PSO-CEMA
## 5 Positioning and Orbit Determination of Space Targets
### Gaussian Minimum Mean Square Error Correction Positioning Algorithm
The iteration condition can be expressed by:  
\[\delta J\equiv\frac{|I_{k}-J_{k-1}|}{J_{k}}<\frac{\varepsilon}{\|W\|} \tag{29}\]  
Orbital position solution, the Equations (12) and (13) can be transformed as follows:  
\[\tan\alpha\ =\ \frac{y_{deb}-y_{sat}}{x_{deb}-x_{sat}}+\
u_{\alpha} \tag{30}\]  
\[\tan\delta\ =\ \frac{z_{deb}-z_{sat}}{\sqrt{\left(x_{deb}-x_{sat}\right)^{2}+ \left(y_{deb}-y_{sat}\right)^{2}}}+\
u_{\delta} \tag{31}\]  
\(\
u_{\alpha}\), \(\
u_{\delta}\) is the tangent error of the rectascension and declination caused by the measurement error of the satellite optical observation platform, respectively. Let vector \(Y\ =\ [\tan\alpha\ \ \tan\delta]^{T}\), then we have:  
\[Y\ =\ [\tan\alpha\ \ \tan\delta]^{T}\ =\ \left[ \begin{array}{cc}\frac{y_{ddb}-y_{sat}} {x_{ddb}-x_{sat}}\\ \frac{z_{ddb}}{z_{ddb}-z_{sat}}\\ \frac{1}{\sqrt{(x_{ddb}-x_{sat})^{2}+(y_{ddb}-y_{sat})^{2}}}\end{array} \right] \tag{32}\]  
The measured sensitivity matrix is:  
\[ \begin{array}{l}H\ =\ \frac{\Delta t_{b}}{\Delta t_{deb}}\\ \ =\ \left[\begin{array}{cc}\frac{\partial t_{1}}{\partial x_{ddb}}&\frac{ \partial t_{1}}{\partial y_{ddb}}&\frac{\partial t_{1}}{\partial z_{ddb}}\\ \frac{\partial t_{2}}{\partial x_{ddb}}&\frac{\partial t_{2}}{\partial y_{ddb} }&\frac{\partial t_{2}}{\partial z_{ddb}}\end{array}\right]\\ \ =\ \left[\begin{array}{cc}-y_{ddb}-y_{sat}\\ -(x_{ddb}-x_{sat})^{2}&\frac{1}{x_{ddb}-x_{sat}}&0\\ -(z_{deb}-z_{sat})(x_{deb}-x_{sat})A^{3}&-(z_{deb}-z_{sat})(y_{deb}-y_{sat})A ^{3}&A\end{array}\right]\end{array}  \tag{33}\]  
where \(A\ =\ \frac{1}{\sqrt{(x_{ddb}-x_{sat})^{2}+(y_{ddb}-y_{sat})^{2}}}\), vector \(Y\) is a nonlinear equation about the debris vector \(r_{deb}\ =\ [x_{deb}\ \ y_{deb}\ \ z_{deb}\ \  
utilization rate of data. The maximum interval in the simulation is 10 h, which can meet most of the requirements of interrupted trajectory association.
# An Improved Reference Paper Collection System Using Web Scraping with Three Enhancements
## 9 Evaluation and Analysis
### Performance Comparison with Common Literature Search Tools  
The performance comparison results show that the proposal outperforms our previous work. The comparison with common literature search tools is important to understand how the proposal meets practical standards for academic researches in a _reference paper collection system_. We conducted the comparison with common literature search tools such as _Publish or Perish_[24], _OpenAIRE_[25], _OpenAlex_[26], and _Google Scholar_[27]. The performance metrics include the number of collected papers, the number article of _open_ and _closed access_, their relevance, PDF accessibility, and the response time. Evaluating the number of _open-access_ articles is important, since some platforms may provide _open-access_ articles where their PDF files are inaccessible.
# Comparative Study Between Active AMD and ABS Devices by Using \(\mu\)-Synthesis Robust Control
## 4 Results
### Worst Case Analysis  
To evaluate the robustness of the synthesis control method in the face of uncertainties, it is essential to analyze the outcomes under two conditions: when uncertainties are at their central values, referred to as the 'Nominal Case', and when uncertainties reach their extreme values, termed the 'Worst Case'.  
We calculated the structural RMS quantities before and after controlling the displacements of the floors where the control systems were located, specifically the first and third floors, to thoroughly examine the robustness of the designed controllers. We performed this by varying the system uncertainties, specifically the stiffness parameters, to their lowest values, as shown in Figures 16 and 17, respectively. Thus, these figures represented the RMS quantities of the displacements of the considered floor before control and the two cases after control:  
* The nominal situation arose when the stiffness parameters were at their median values, corresponding to 98 kN/m;
* When these parameters fluctuated to their extreme values, \(\pm\)30% of their nominal values were referred to as the worst scenario, resulting in the poorest performance, which corresponds to 68.6 kN/m.  
Figure 16: First floor displacements comparison in nominal and worst case with ABS and AMD systems.  
Figure 15: Comparison of RMS values of accelerations with AMD and ABSs.  
Both of the figures distinctly illustrate that, even at peak uncertainty, the structure's responses to seismic loads exhibited consistent levels of attenuation, with just little discrepancies in the attenuation results between the two controller's \(\mu_{\text{C-MD}}\), and \(\mu_{\text{C-BS}}\).  
Indeed, the controller \(\mu_{\text{C-MD}}\) achieved the worst-case floors' displacements attenuation percentages of 64%, 67%, and 70% for the first, second, and third floors, respectively, while the controller \(\mu_{\text{C-BS}}\) achieved these percentages of 53%, 66%, and 67%. The performance of the two controllers in the bad cases was actually very satisfactory, attesting to the robustness of the control method applied.
# Multi-Year Monitoring of Ecosystem Metabolism in Two Branches of a Cold-Water Stream
## 1 Introduction
Even though small streams have relatively low GPP, they can disproportionately affect the GPP of a river network because of their large overall surface area [42]. Despite the potential usefulness of using measures of ecosystem metabolism to track the impact of climate change on streams, few studies have looked at interannual variability, which is necessary to track long-term trends in warming. Large river systems can have substantial interannual variation in measures of ecosystem metabolism, often related to stochastic events, although seasonal variation can account for about half of the total variation [43]. In headwater streams, interannual variability also seems to be controlled mainly by local stochastic events (storm frequency and macroalgal blooms) or global events (El Nino--Southern Oscillation) [44; 45]. Interannual variability can also be influenced by varying human land use over time, and thus, long-term temporal monitoring is required to assess how streams respond to changes in both natural and human impacts [36]. In this study, ecosystem metabolism was assessed in two branches of a cold-water stream. Metabolism in these locations was studied in the fall of 2015 by [PERSON] et al. [46]. One location was generally colder than the other but unexpectedly had higher GPP. While this location had similar dissolved phosphate levels, it did have higher levels of nitrate [46]. Several studies have indicated that warmer temperatures result in increasing rates of GPP and ER [47; 48; 49]. The goal of this study was to examine whether the differences in ecosystem metabolism between the two locations persisted over seasons and in different years and if groundwater input also varied. It was predicted that gross primary production and ecosystem respiration would be greater when temperatures were warmer both seasonally and between years and that the streams might be autotrophic in the spring when light availability is greatest (less shading from the surrounding forest). It was also expected that higher discharge might influence ecosystem respiration bringing in more allochthonous material to be decomposed.
## 4 Hardware Customization
### Accessories Detection and Health Mote
#### 4.1.2 Worker Health Monitoring Note  
Adults and elders usually work in the construction field and they work very hard continuously throughout the day, so that monitoring their health parameters is very important in predicting any health issues in them. The health parameters of the worker, such as body temperature and pulse rate, are continuously measured using this mode and updates the same information through RF communication, as shown in Figure 9a.  
The body temperature and pulse rate are basic health parameters that assist in finding any abnormal health condition of the workers. This mode alerts the main server to take necessary action immediately and provide assistance to the worker to save their health before it becomes a critical condition. This mode runs on battery power and moreover the components embedded in the mode are compatible with the battery power supply, as they are low power consuming components.  
The customized hardware for the health detection of workers is shown in Figure 9b, an infrared-based non-contact temperature sensor MLX90614 is used to detect the temperature, it is capable of measuring the temperature in the range of \(-70\)\({}^{\circ}\)C to \(382\)\({}^{\circ}\)C and communicates through a serial inter-integrated communication (I2C) protocol and a pulse sensor is used to monitor the heart rate. This mode is interconnected with the shoe detection system and gloves' detection system through RF communication, technical specifications of each and every module used in worker health monitoring mode are listed in Table 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Specifications** \\ \hline Microcontroller: & ATMega328P \\ Communication module: & nRF24L01+ Wireless module (2.4 GHz) \\ Voltage converter: & +5 V \\ Eye Blink Sensor: & Infrared-based sensor \\ RFID Tag: & 125 KHZ RFID Tag \\ Power trails: & +5 V \\ Power jack: & +5 V External power supply \\ Programming pins: & FTDI and ICSP \\ \hline \hline \end{tabular}
\end{table}
Table 2: Technical Specifications of Helmet Detection Note.  
Figure 9: (**a**) Worker Health Monitoring Note; (**b**) Customized Hardware of Worker Health Monitoring Note.
# Subpath Queries on Compressed Graphs: A Survey
## 3 Indexing Labeled Graphs and Regular Languages
### Indexing Labeled Trees
#### 3.5.2 The XBW Transform  
One disadvantage of the prefix array is that, like the SA seen in Section 2.1, it does not achieve compression. In addition to the \(n\log n\) bits for the prefix array itself, the above binary-search strategy also requires us to navigate the tree (for which topology and labels must therefore be kept in memory). This is much more space than the labels, which require \(n\log\sigma\) bits when stored in plain format, and the tree topology, which can be stored in just \(2n\) bits (for example, in balanced-parentheses sequence representation). In 2005, [PERSON] et al. [35; 68] observed that this space overhead is not necessary: an efficient search machinery can be fit into a space proportional to the entropy-compressed edge labels, plus the succinct (\(2n+o(n)\) bits) tree's topology. Their structure takes the name _XBW tree transform_ (XBWT in the following), and is a compressed tree representation  
Figure 7: Example of labeled tree. Nodes have been enumerated in pre-order. The list of nodes sorted by the co-lexicographic order of the paths connecting them to the root (that is, the prefix array (PA) of the tree) is: 1, 2, 3, 4, 7, 6, 9, 5, 8.  
natively supporting subpath search queries. Consider the co-lexicographic node ordering of Figure 7, denote with \(child(u)\) the multi-set of outgoing labels of node \(u\) (for the leaves, \(child(u)=\emptyset\)), and with \(\lambda(u)\) the incoming label of node \(u\) (for the root, \(\lambda(u)=\$\), where as usual \(\$\) is lexicographically smaller than all other characters). Let \(u_{1},\ldots,u_{n}\) be the prefix array of the tree. The XBWT is the pair of sequences \(IN=\lambda(u_{1})\ldots\lambda(u_{n})\) and \(OUT=child(u_{1}),\ldots,child(u_{n})\). See Figure 8 for an example based on the tree of Figure 7. For simplicity, the tree of this example is a trie (that is, a deterministic tree). All of the following reasoning, however, applies immediately to arbitrary labeled trees.
# Utilization of Polymeric Materials toward Sustainable Biodiesel Industry: A Recent Review
## 4 Cold-Flow Improvers
The last approach is using additives to enhance the biodiesel's cold-flow properties. Multiple kinds of additives have been reported in the literature such as metal additives, oxygenated-agents additives, antioxidants additives, and CFIs. Literature reported enhanced performance and economic benefits as a result of using additives to enhance cold-flow properties of biodiesel fuel [99; 100]. In this regard, polymers were used in biodiesel industry as CFIs. Polymers additives create modification in the structural agglomeration of biodiesel crystals at lower temperatures. With the polymeric materials, crystals take the needle shape, which eases the flowability through filters, pipes, and injectors in the engine's fueling system [101]. Here, there are twotypes of polymeric CFIs, the first type is for the pure biodiesel and the other is used for blended biodiesel/diesel fuel.
# Environmentally Friendly Smart Construction--Review of Recent Developments and Opportunities
### Resilient Structures Based on Topological Interlocking  
A new opportunity for designing resilient structures arises in the utilisation of topologically interlocked (TI) structures. A TI assembly consists of building blocks with special shapes, which are arranged such that no internal block can be extracted from the structure without disturbing its neighbours. The peripheral blocks are constrained and the entire structure maintains its integrity without the help of any binder or connectors [180; 181] (Figure 7). In construction engineering, the peripheral blocks can be secured in place either by the weight of the blocks themselves or by employing constraining frames, pre-tensioned tendons or cables [181].  
The absence of keys or connectors between the blocks in TI assemblies clearly distinguishes TI blocks from the conventional, self-locking blocks used in the construction industry. The locks and connectors used in the latter introduce high stress concentrations and thus reduce the structure's overall strength. In addition, the connectors (or the mortar in the brick-and-mortar construction) stiffen the structure, which reduces its resilience to vibrations and seismicity. Topological interlocking, in contrast, allows for some relative movement between the blocks, which provides a mechanism for containing high-amplitude vibrations (e.g., seismic or blast vibrations) and for the dissipation of the energy due to block rotations and friction between the moving surfaces [281]. Research in the past decade indicates that TI structures offer a wide range of useful properties that meet the requirements of resilient design. In particular, extremely high energy absorption [218; 240], tolerance to large deflections [181], enhanced fracture resistance [201; 227], high impact resistance [282], high tolerance to missing blocks of certain shapes [283], high adaptivity [221; 284], ductility [285], reparability and recyclability [222; 286] are critical for the design of resilient structures.
# Toward QoS Monitoring in IoT Edge Devices Driven Healthcare--A Systematic Literature Review
## 3 The Systematic Review Process
### Research Questions  
Raising the proper questions makes review studies valuable and significant to the research community since they influence the entire SLR process. Review questions explain any changes or evolution in the SLR topic's status and suggest if these changes should be accepted as part of the body of knowledge [10]. Questions were organized in a hierarchy  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Links** & **Strength** & **Information** \\ \hline IoT \(\leftrightarrow\) ML & strong & more research articles published \\ IoT \(\leftrightarrow\) Cloud Computing & strong & more research articles published \\ Cloud Computing \(\leftrightarrow\) ML & weak & fewer research articles published \\ \hline \hline \end{tabular}
\end{table}
Table 1: Link strength analysis among IoT, ML, and cloud computing.  
Figure 4: Keywords metefacto indicates the hierarchy of included and excluded keywords [10].  
of fundamental inquiries to ongoing research difficulties to provide readers a thorough understanding of the subject. Additionally, RQs were in line with the research objectives and the most up-to-date procedures and techniques being used in SHAs. The research topics that follow are intended to reveal the state of the art in QoS optimization for healthcare applications. The following research questions are answered in this review:  
* Which fundamental enabling technologies are primarily used in today's smart healthcare applications?
* What is the scope of QoS optimization in recent smart healthcare applications?
* Which QoS parameters are mostly employed in current smart healthcare applications?
* What are the current QoS aware technologies, methods, tools, and datasets employed in healthcare applications?
* What are the current QoS optimization challenges and solutions in smart healthcare applications (SHAs)?