esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Method
### Pose Refinement Process
#### 3.1.4 Interpolating Missing Coordinates  
The algorithm for interpolating missing coordinates after outliers are removed through the outlier detection model is represented in Figure 4e. There are interpolation methods, such as Linear, Next, Previous, and Nearest, and, through performance comparisons of each method, it was found that the Linear method shows high performance. The Next and Previous methods fill the missing space by duplicating the values from the subsequent and preceding data points, respectively. The Nearest method fills the missing space by duplicating the value from the data point that has the closest x-value. However, these approaches simply duplicate the existing value, so these do not reflect the changes between two data points. Linear Interpolation is a method used to estimate the value of f(x) for an x-value between two given points. This method allows for more accurate interpolation of values because it takes into account the movement path and changes over time, reflecting the variations in each frame.
# Research on Decision-Making Methods for Autonomous Navigation in Inland Tribitary Waterways
## 4 Case Study
### The Verification of the Autonomous Navigation Decision-Making
#### 4.2.3 Multi-Ship Encounter Scenario 3  
The initial states and steering parameters of the ships in the multi-ship encounter scenario within the tributary channel under the TSs' maneuvering conditions are provided in Table 5.  
The initial encounter situation mirrors the multi-ship scenario described in the previous section, with the exception that TS\({}_{7}\) initiates a 30\({}^{\circ}\) portside turn at 50 s, TS\({}_{3}\) starts to turn 55\({}^{\circ}\) to starboard at 60 s, and [PERSON]\({}_{1}\) starts to turn 55\({}^{\circ}\) to starboard at 115 s. The autonomous navigation decision-making process and results for this modified scenario are illustrated in Figure 19.  
Initially, to avoid TS\({}_{3}\), the OS proactively implements a deceleration maneuver. At 60 s, [PERSON]\({}_{3}\) alters its course from 355\({}^{\circ}\) to 30\({}^{\circ}\), taking collision avoidance action that eliminates the risk with the OS. Concurrently, [PERSON]\({}_{7}\) begins adjusting its course from 160\({}^{\circ}\) to 130\({}^{\circ}\) at 50 s, inadvertently escalating collision risks with the OS. At this point, deceleration alone proves insufficient for the OS to avoid TS\({}_{7}\). Consequently, the OS combines deceleration with turning to starboard, safely passing astern of TS\({}_{7}\) at 173 s. When no initial collision risk exists between TS\({}_{1}\) and the OS, TS\({}_{1}\) initiates a starboard turn at 115 s. Based on turn rate calculations, it is projected that TS\({}_{1}\) will exit the tributary channel and cross the OS's starboard side, creating a collision risk. To mitigate this, the OS maintains its deceleration strategy, completed overtaking TS\({}_{4}\) at 304 s, and ultimately clear TS\({}_{1}\) at 337 s.  
As shown in the results of Figure 20, the minimum distances between the OS and TSs during autonomous navigation are as follows: 131 m to TS\({}_{1}\), 54 m to TS\({}_{2}\), 353 m to TS\({}_{3}\), 455 m to TS\({}_{4}\), 145 m to TS\({}_{5}\), 111 m to TS\({}_{6}\), and 61 m to TS\({}_{7}\).  
Figure 18: Multi-ship encounter data of scenario 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Ship List** & **Position** & **Velocity (kn)** & **Course (\({}^{\circ}\))** & **Turning Rate (\({}^{\circ}\)/s)** & **Turning Angle (\({}^{\circ}\))** \\ \hline OS & \(119^{\circ}28.782^{\prime}\) E, \(32^{\circ}16.302^{\prime}\) N & 12.5 & 275 & — & — \\ TS\({}_{1}\) & \(119^{\circ}27.906^{\prime}\) E, \(32^{\circ}16.704^{\prime}\) N & 6.5 & 180 & 1 & 55 \\ TS
# Static Magnetic Fields Effects on Polysaccharides Production by Different Microsalgae Strains
###### Abstract  
Microalgae are able to produce many valuable biomolecules, such as polysaccharides, that presents a large diversity of biochemical structures and functions as antioxidant, antifungal, anticancer, among others. Static magnetic fields (SMF) influence the metabolism of microorganisms and has been shown as an alternative to increase microalgae biomass, yield and compounds production. Especially, some studies have highlighted that SMF application could enhance carbohydrate content. This study aimed to evaluate different conditions of SMF on _Spiulina_ and _Chorella_ in indoor and outdoor conditions, in order to confirm the influence of SMF on polysaccharides production, evaluating which polysaccharide fraction could be enhanced by SMF and highlighting a possible modification in EPS composition. Starch from _Chorella_ and exopolysaccharideanradides (EPS) from _Spiulina_ were quantified and characterized. SMF increased the starch content in _Chorella fusca_ biomass. EPS productions from _A. platensis_ and _Spiulina_ sp. were not significantly increased, and global composition appeared similar to the controls (constituted basically of 80-86% neutral sugars and 13-19% unroix acids). However, the monosaccharide composition analysis revealed a significant modification of composition, i.e., the amount of fucose, arabinose, hamnose, galactose and glucuronic acid was increased, while the glucose content was decreased. SMF application led to significant modification of polysaccharides production and this study demonstrate that combining the outdoor conditions with SMF, the starch content and EPS composition was positively affected.  
static magnetic fields (SMF); _Spiulina_; _Chorella_; excoplysaccharides; starch 2021115299. [[https://doi.org/10.3390/app11115299](https://doi.org/10.3390/app11115299)]([https://doi.org/10.3390/app11115299](https://doi.org/10.3390/app11115299))
## 4 Proposed Methodology
### Solution Based on the Recommendation System
#### 4.2.4 Content-Based Filtering  
As illustrated in the pseudo-code Algorithm 3, in this step, after retrieving data on the user details (the history), its requested service requirements, and Fog node details (i.e., memory, storage, and CPU), the system calculates the cosine similarity between the target service's characteristics and the characteristics of all colony nodes.  
The cosine similarity is computed as follows:  
\[\text{Similarity}(s_{t},n_{j})=\frac{\sum_{i=1}^{n}R_{t,i}\cdot N_{j,i}}{ \sqrt{\sum_{i=1}^{n}R_{t,i}^{2}\cdot\sqrt{\sum_{i=1}^{n}N_{j,i}^{2}}}} \tag{3}\]  
where  
* \(R_{t,i}\) is the value of feature \(i\) (e.g., CPU, RAM, storage, and delay) in the requirement vector of the target service \(s_{t}\).
* \(N_{j,i}\) is the value of feature \(i\) (e.g., CPU, RAM, storage, and delay) in the feature vector of colony node \(n_{j}\).
* \(n\) is the number of features considered (e.g., 4 features: CPU, RAM, storage, and delay).  
In the content-based filtering stage, the system considers the top \(K=3\) colony nodes ranked by cosine similarity with the target service request. The value of \(K\) was chosen based on its effectiveness in reducing deployment delay.  
After ranking the nodes based on similarity, the system checks if the target user has previously used the most similar node. That node is immediately recommended if it exists in the user's history. If not, the system checks the next most similar node in the top K list. If no suitable node is found in the history, the node with the highest similarity is recommended as the optimal location for the service.  
```
1:Input: Requirement vector \(R_{t}\), Node set \(\mathcal{N}\), User history \(H\)
2:Output: Recommended node \(n_{r}\)
3:functionContentBasedFiltering(\(R_{t}\), \(\mathcal{N}\), \(H\))
4: Initialize similarity list \(L\leftarrow[\;]\)
5:for each node \(n_{j}\) in \(\mathcal{N}\)do
6: Let \(N_{j}\) be the feature vector of node \(n_{j}\)
7: Compute cosine similarity between \(R_{t}\) and \(N_{j}\)
8: Append \((n_{j},\text{similarity})\) to \(L\)
9:endfor
10: Sort \(L\) in descending order of similarity
11: Select top \(K\) nodes: \(N_{topK}\leftarrow\) first \(K\) nodes from \(L\)
12:for each node \(n\) in \(N_{topK}\)do
13:if\(n\in H[u_{t}]\)and\(n\) is available then
14:return\(n\)
15:endif
16:endfor
17:for each node \(n\) in \(N_{topK}\)do
18:if\(n\) is available then
19:return\(n\)
20:endif
21:endfor
22:return NULL \(\triangleright\) No suitable node found
23:endfunction
```  
**Algorithm 3** Content-Based Filtering
# UAV Based Relay for Wireless Sensor Networks in 5G Systems
## 2 System Model and the Problem Formulation
Figure 1: System model of the unmanned aerial vehicle (UAV) based relay in wireless sensor networks (WSN). The key issue is to determine the specific position of the UAV at the line segment \(\overline{BS}\). We define the length of the line segment \(\overline{US}\) as \(x\), the length of \(\overline{BS}\) is \(T\), then the length of \(\overline{BU}\) is \(T-x\). Assume that the demand of traffic capacity is \(C\) bit/s (bps) for transmission of the concentrator. To avoid packet loss at the UAV, the capacity for transmission of UAV is also \(C\) bps. Let \(\sigma_{S}\) and \(\sigma_{U}\) denote the noise power received at UAV and BS, respectively. According to Shannon capacity, we have  
\[C=B_{S}\ \log_{2}\left(1+\frac{P_{S}|h_{S}|^{2}}{\sigma_{S}{}^{2}}\right)=B_{U} \ \log_{2}\left(1+\frac{P_{U}|h_{U}|^{2}}{\sigma_{U}{}^{2}}\right), \tag{4}\]  
\[|h_{S}|^{2}=10^{-\frac{L_{S}}{10}}=10^{-\frac{86.6+245.5\cdot\log_{10}x}{10}}, \tag{5}\]  
\[|h_{U}|^{2}=10^{-\frac{L_{U}}{10}}=10^{-\frac{86.6+245.5\cdot\log_{10}(T-x)}{1 0}}. \tag{6}\]  
By (4), we can further prove the transmitting power of the concentrator and UAV as  
\[P_{S}=\frac{\sigma_{S}{}^{2}}{|h_{S}|^{2}}\bigg{(}2^{\frac{C}{B_{S}}}-1\bigg{)} =\frac{\sigma_{S}{}^{2}}{10^{-\frac{86.6+245.5\cdot\log_{10}(T-x)}{10}}} \bigg{(}2^{\frac{C}{B_{S}}}-1\bigg{)}, \tag{7}\]  
\[|h_{S}|^{2}=10^{-\frac{L_{S}}{10}}, \tag{2}\]0  
The objective is to minimize the system transmitting power in WSN. We denote the total transmitting power in the system as \(P_{\max}\), then, we can obtain the system optimization model as  
\[|h_{S}|^{2}=10^{-\frac{L_{S}}{10}}, \tag{2}\]1
## 3 Physiological Processes Mediating Cadmium Accumulation in Plants
### Root-to-Shoot Cd Translocation  
After its uptake by the root epidermis or exodermis, Cd is radially transported across the cortical, endodermal, and pericycle cells and then loaded into the root xylem for its subsequent root-to-shoot translocation via the xylem [71; 72; 73]. Loading Cd into the root xylem is a crucial step for Cd translocation to the aerial plant parts [4; 73]. The radial movement of Cd toward the root xylem occurs via its symplastic and/or apoplastic transport in the form of free Cd\({}^{2+}\) or Cd-complexes with various chelates [7]. Symplastic transport is considered an energy-consuming (positive) pathway involving both influx and efflux transporters [6]. Apoplastic transport is a passive pathway, usually driven by transpiration [48]. It has been suggested that symplastic transport plays a dominant role in the radial transport of Cd [71; 72]. However, [PERSON] et al. [74] reported that the apoplastic pathway contributed up to 37% of the Cd transported to the xylem in _Sedum alfredii_. After crossing the root epidermis to the root cortex barrier, free Cd\({}^{2+}\) or Cd chelates may enter the symplasm and are then loaded into the root xylem [3]. The xylem loading of Cd in roots is an energy-consuming process because it occurs against the membrane potential (xylem parenchyma cells, \(-110\) to \(-150\) mV; xylem vessels, \(-90\) mV) [6] and is mediated by heavy metal P1B-ATPases (HMAs) and possibly also by yellow stripe-like (YSL) proteins [4; 64].
# Boosting the Performance of CDCL-Based SAT Solvers by Exploiting Backbones and Backdoors
## 2 Definitions and Notations
### Boolean Satisfiability Problem  
A Boolean variable \(x\) may take the value \(true\) or \(false\). A literal is a Boolean variable \(x\) or its negation \(\overline{x}\). A clause \(C\) is a disjunction of literals. Given a CNF formula \(\phi\) defined over \(X\), where \(X=\{x_{1},x_{2},\ldots,x_{n}\}\) is a set of Boolean variables and \(L=\{x_{i},\overline{x}_{i}|x_{i}\in X,\)\(1\leq i\leq n\}\) is a set of literals over \(X\), the following definitions hold:  
**Definition 1** (Conjunctive Normal Form (CNF) formula).: _A CNF formula \(\phi\) is a conjunction of clauses, which is represented as a multiset of clauses \(\{C_{1},C_{2},\ldots,C_{m}\}\)._  
**Definition 2** (\(k\)-CNF formula).: _A \(k\)-CNF formula is a CNF formula with exactly \(k\) literals per clause._  
**Definition 3** (Assignment).: _An assignment is a mapping from each variable \(x_{i}\) to \(\{true,false\}\). This is denoted by:_  
\[\phi|_{\{x_{i}=value_{i}|\forall x_{i}\in X,value_{i}\in\{true,false\}\}}\]  
**Definition 4**.: _An assignment satisfies a literal \(x_{i}\) if \(x_{i}=true\), and satisfies a literal \(\overline{x}_{i}\) if \(x_{i}=false\)._  
**Definition 5**.: _An assignment satisfies a clause \(C_{i}\) if it satisfies at least one literal in \(C_{i}\)._  
**Definition 6**.: _An assignment satisfies a CNF formula if it satisfies all clauses._  
**Definition 7**.: _A CNF formula \(\phi\) is satisfiable if there exists an assignment that satisfies \(\phi\), that is:_  
\[\phi|_{\{x_{i}=value_{i}|\forall x_{i}\in X,value_{i}\in\{true,false\}\}}=true\]  
_Otherwise, the formula \(\phi\) is unsatisfiable:_  
\[\phi|_{\{x_{i}=value_{i}|\forall x_{i}\in X,value_{i}\in\{true,false\}\}}=false\]  
**Definition 8** (SAT problem [1]).: _Given a CNF formula \(\phi\) over \(X\), the SAT problem asks whether there is an assignment that satisfies \(\phi\), or it decides that the formula is unsatisfiable._  
**Definition 9** (SAT solver).: _A SAT solver is a computer program which aims to solve the SAT problem._
# The Use of Smart Rings in Health Monitoring--A Meta-Analysis
## 4 Discussion
### Meta-Analysis on Sleep Outcomes  
The meta-analyses on sleep outcomes revealed a considerable variability in the accuracy of SRs in analysing different sleep phases, due to the between-studies heterogeneity in the point estimates for mean biases and the considerable width of the LoAs confidence intervals. The considerable variability across studies may result from the lack of standardization in recording duration. This diversity in study protocols may impact the accuracy and reliability of sleep quality assessments using SRs, leading to the observed heterogeneity in outcomes. Future research should standardize protocols for SR validation in sleep monitoring to ensure more consistent and clinically meaningful results. Additionally, given the potential impact of smart ring technology on public health and quality of life, further exploration of its role in sleep quality monitoring is crucial [57]. In a study by [PERSON] et al. [58], results indicate that duration of REM sleep may be a greater contributor to brain white matter integrity than overall sleep quality. Sleep deprivation, both in quality and duration, is associated with increased mortality risk [59; 60]. This underlines the potential importance and benefits of monitoring REM and sleep time. As SRs may be suitable for this purpose, further studies analysing their accuracy are needed. Furthermore, SRs only cost USD 100-USD 300, a fraction of the costs associated with the equipment and expertise needed for PSG. Thus, these devices are potentially cost-effective sleep measurement  
Figure 4: (**A**) Cardiovascular studies: control device. PSG: polysomnography. ECG: Electrocardiogram. BP: Blood Pressure. (**B**) Cardiovascular studies: smart ring type. Gen: generation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Mean Bias (min)**} & **SD** & **LoAs** & **CI\_Lm** & **CI\_Um** \\ \hline \multicolumn{1}{c}{Nocturnal HR} & \(-\)0.4 & 1.1 & \(-\)2.7, 1.8 & \(-\)6.0 & 5.1 \\ \hline \hline \multicolumn{5}{l}{Abbreviations: SD = Standard Deviation, LoAs = limits of agreement, CL Lm = 95\% confidence interval lower bound of the lower LoA, CI\_Um = 95\% confidence interval upper bound of the upper LoA.} \\ \end{tabular}
\end{table}
Table 3: Results of the meta-analyses of the accuracy of Oura Ring® in monitoring cardiovascular parameters compared to PSG.  
tools [48]. However, no study has assessed both the direct and indirect cost and considered all the health and economic benefits.  
The included studies varied by study population, potentially affecting the generalizability of the results. This could also partly explain the wide uncertainty that emerged from the meta-analytical findings, since sensitivity analyses on Oura(r) accuracy in measuring REM, obtained by excluding the results from the one study on adolescents [42], showed an important reduction in LOAs' and CIs' width, denoting that different age groups may respond differently to sleep quality measurements.
# Stream Data Load Prediction for Resource Scaling Using Online Support Vector Regression
## 3 Task Description  
### System Aarchitecture  
The system architecture of this paper is shown in Figure 1. The Producer sends data to Kafka, which is an efficient distributed messaging system in period \(t\). Spark extracts data from Kafka in real time and handles it at the interval of time window \(t\). A Spark cluster contains multiple work nodes, each of which contains one or more executor. Producer sends data to Kafka in real time, and Kafka stores the data in Partition as a unit. After Spark draws the Kafka data, Spark converts this data into a series of RDD for processing, and the internal structure of RDD is also presented in a partitioned form. Processing of RDD partition data within a time window \(t\) should be submitted to executor in the Spark cluster work node and execute it as a task. An RDD data corresponds to the cumulative data that Spark reads from Kafka in a time slice. The number of partitions in Kafka, partition in RDD and the number of tasks executed in the end must be consistent. There are three core processes of the system framework: the load forecasting model, the performance model, the flexible resource distribution. The load forecasting model predicts the load trend of the next window \(t\) according to historical data of the stream data load. The performance model is obtained by monitoring the throughput of the Spark processing data and the batch response time and so on. The flexible resource distribution will get the timing of the flexible operation based on the relation of load of next \(t\) and the performance model, and assigns a reasonable number of virtual machines to the Spark cluster before the next time window \(t\)
# An Intelligent Approach for Contact Pressure Optimization of PEM Fuel Cell Gas Diffusion Layers
## 4 Results and Discussion
### Distribution of Contact Pressure  
The contact pressure contour of the GDL can be broken down into two directions, as shown in Figure 8. Figure 8a shows the contact pressure under a rib along the channel direction. The GDL under a rib is compressed tightly at the inlet and outlet and loosely in the middle, which shows a basin curve, with around 0.3 MPa difference between the peak and the valley. At the ends close to bolts, a maximum 0.75 MPa contact pressure means the GDL is compressed a lot and loses porosity. With increasing distance away from the bolts, the contact pressure drops sharply and then goes stable at around 0.48 MPa at the bottom. In this case, the whole rib-GDL contact area shows two different patterns according to the contact pressure. The bottom area in the middle section, taking about 50% of the channel length, holds a stable contact pressure. The contact pressure of the outer sections near the end of the channel grows sharply and reaches the peak at the inlet and outlet.  
In the cross-channel direction, as shown in Figure 8b, the contact pressure fluctuates in a \"rib edge-rib center-channel\" pattern. The sharp peaks represent the stress concentrations at the rib edges because the contact form changes from channel-GDL (non-contact) to rib-GDL (solid-contact). So, the zero-contact pressure means the GDL is not compressed at all under the channel. Valleys around 0.6 MPa between rib edges represent the most of the GDL under ribs, which shows a median compression. From the whole width point of view, the contact pressure still performs as a basin shape according to the distance from the edge.  
Overall, the distribution of contact pressure on the GDL oscillates a lot, which is not preferable for maximizing fuel cell performance. The contact in the center needs to be enhanced, and that at the edge needs to be controlled.  
Figure 7: Comparison of contact pressure between (**a**) experiment and (**b**) FE simulation.
# Q8S: Emulation of Heterogeneous Kubernetes Clusters Using QEMU
## 1 Introduction
### Related Work
CloudSimSC [32], similar to faas-sim provides tools for simulating serverless applications and bases its assumptions about function life cycles on Kubernetes. However, it explicitly focuses on data center deployments and does not further consider heterogeneous environments. iContinuum [18] proposes an emulation toolkit for cloud-edge environments with IoT devices. It also argues for using emulations for higher accuracy compared to simulations as nuanced interactions can be observed, which would be missing from the abstractions used in simulations. The emulated environment utilizes Kubernetes with Mininet to emulate network topologies. By incorporating edge devices such as Raspberry Pis, it is possible to construct heterogeneous clusters. Emulation of edge hardware is not supported by iContinuum. Large-Scale Cloud Emulation [20] provides an emulation approach for large clouds, i.e., tens of thousands to hundreds of thousands of nodes. For this, it utilizes densely packed node emulations on top of Kubernetes clusters. Overall, these related works display many simulation approaches focusing on various aspects of cloud computing such as support for complex network topologies [15], heterogeneous resources [14], or even specific applications such as serverless computing [31; 32]. Moreover, some of these approaches take inspiration from or mimic Kubernetes scheduling [17; 31; 32]. However, as argued by [18], simulations are limited in the accuracy they can provide, owing to the level of abstraction their models utilize. Frameworks such as [18; 20] provide more details through emulation, but in order to support heterogeneous hardware, they require physical access to such hardware. Table 1 summarizes the capabilities of the above-discussed frameworks that employ emulation and the features that they support. EMUSIM is a simulation framework, but it is included here, as it employs sampling of workloads in emulated environments. The other frameworks fully focus on emulation, and each one provides features specialized for certain use cases. iContinuum supports complex network topologies as found in edge network setups. Large-Scale Cloud Emulation supports emulations with many nodes by packing multiple nodes on the same host. Q8S, on the other hand, employs hardware emulation, enabling running emulations even when only homogeneous hardware is available.
* Section 6--Trajectory Refinement--describes the trajectory refinement process. * Section 7--Horizon-Based Trajectory Planning--presents horizon-based trajectory planning techniques, starting with linear quadratic regulator (LQR) and its variants, such as iterative LQR (iLQR) and extended LQR (ELQR). It then covers advanced techniques, such as model predictive control (MPC) and its variants, including nonlinear MPC (NMPC). * Section 8--Solvers for Optimization--details various solvers that can be used to solve the optimization problem, starting with quadratic programming formulation. It then lists and describes the usage of mixed-integer quadratic programming (MIQP), gradient-based trajectory optimization (GTO), BOBYQA, and many other solutions.