esa-sceva/satcom-chunk-collection huggingface dataset:
# A Topology Map Generation Algorithm for Optimal Path Finding for Image-Based Maps
## 5 Experimental Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Complexity** & **A*** & **A* With CNA** & **Percentage** \\ \hline Very Simple & 46,062.5 & 39 & \(-\)99.92\% \\ \hline Simple & 122,703 & 102.2 & \(-\)99.92\% \\ \hline Intermediate & 107,462.6 & 147.2 & \(-\)99.86\% \\ \hline Complicated & 176,671.1 & 200.8 & \(-\)99.89\% \\ \hline Very Complicated & 759,311.4 & 380.8 & \(-\)99.95\% \\ \hline Special Case & 143,376.6 & 260 & \(-\)99.82\% \\ \hline \hline \end{tabular} A* is the average number of nodes visited when A* is used alone. A* with CNA is the average number of nodes visited when the CNA-generated topology map and A* are used together. A value obtained by adding the number of List and Close List was used. Percentage represents the increase/decrease value of A* and A* with CNA, rounded to the third decimal place.  
\end{table} 
Table 4: Experimental results. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Complexity** & **Preprocessing Time** & **A*** & **A* with CNA** & **Percentage** \\ \hline Very Simple & 7 ms & 75.8 ms & 1.2 ms & \(-\)90.77\% \\ \hline Simple & 46.2 ms & 264.2 ms & 2.5 ms & \(-\)99.05\% \\ \hline Intermediate & 90.4 ms & 183.8 ms & 2.8 ms & \(-\)98.48\% \\ \hline Complicated & 280.8 ms & 775.5 ms & 6.7 ms & \(-\)99.14\% \\ \hline Very Complicated & 1137.2 ms & 3342.1 ms & 9.7 ms & \(-\)99.71\% \\ \hline Special Case & 582.8 ms & 557.6 ms & 5.5 ms & \(-\)99.01\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Experimental results. Percentage represents the difference (in milliseconds) when A* is used alone and with the Topology Map, rounded to the third decimal place.
## 2 Materials and Methods
### Proposed Machine Learning Models
#### 2.4.2 PLSR Model  
PLSR is very effective at predicting a group of dependent variables from a number of independent variables [46]. This new multivariable statistical data analytical method integrates principal component analysis and multivariable linear analysis. When the variables have a high linear correlation, a very effective PLSR model can be obtained. In this study, PLSR was used as one of the models to predict soil shear strength parameters.  
Basically, PLSR can be divided into two processes [47]. The first involves identifying the principal component factors (PCFs) in the input variable space, and the second involves binding these PCFs to dependent variables; therefore, the number of principal component factors (PCFs) affects the performance of the model. In order to determine the appropriate PCF value, we used leave-one-out cross-validation and Akaike information criterion (_AIC_)to evaluate how the number of PCFs affected the performance of the PLSR model. \(AIC\) is calculated as follows [39]:  
\[AIC=N\cdot\log(RSS)+2p \tag{8}\]  
where \(N\) is the sample size; \(p\) is the number of PCFs; \(RSS\) is the sum of squared residuals for prediction with the PLSR model.  
The PLSR method assumes that the variables from the input space X can be written as a linear combination of vectors t and e (namely scores) and that the variables from the output space \(Y\) can be written as a linear combination of \(q\) and \(u\) (namely loadings) [48]; therefore, these relationships are as follows [49]:  
\[X=\sum_{i=1}^{p}t_{i}e_{i}=TE^{T} \tag{9}\]  
\[Y=\sum_{i=1}^{p}q_{i}u_{i}=QU^{T} \tag{10}\]  
where \(X\) is the input variable space, \(X=\{x_{1},\,x_{2},\,\ldots,\,x_{m}\}\); \(Y\) is the output variable space, \(Y=\{y_{1},\,y_{2},\,\ldots,\,y_{m}\}\); m is the size of the sample; \(p\) is an arbitrary number of principal component factors; \(t\) and \(e\) are parameters of linear combinations for scores; \(q\) and \(u\) are parameters of linear combinations for loadings; \(T\) and \(Q\) are the block scoring matrix of \(m\times p\) dimension; \(E^{T}\) and \(U^{T}\) are the block loading matrix of \(p\times m\) dimension.  
To build the PLSR models of soil cohesion and soil internal friction angle, using the NIPALS (nonlinear partial least squares) iterative algorithm [47], the decomposition of state vectors of the input and the dependent variables was obtained as assumed at the beginning. After that, the performance of the models was evaluated.
# Proposal for the Evaluation of Eco-Efficient Concrete
## 4 Case Analysis
### Results of Eco-Efficiency Assessment for Different Compressive Strengths  
As shown in Table 8, average eco-efficiency increased from 5700 (KRW/year) to 2200 (KRW/year) with an increase of compressive strength from 21 MPa to 40 MPa. In the case of 24 MPa and 40 MPa concretes, average eco-efficiency was 4700 (KRW/year) and 2200 (KRW/year), respectively (Figure 7).  
The eco-efficiency of 40 MPa concrete was increased by about 50% in comparison to the eco-efficiency of 24 MPa concrete.  
As shown in Figure 7, cost (comprising environmental and manufacturing costs) increased by about 21% from 318,000 (KRW/m\({}^{3}\)) to 387,000 (KRW/m\({}^{3}\)) according to increasing compressive strengths from 24 MPa to 40 MPa. Importantly, however, the service life of concrete based on carbonation was greatly increased from 68 (year/m\({}^{3}\)) to 190 (year/m\({}^{3}\)) with rising compressive strengths as well. As such, eco-efficiency was found to increase with the increasing compressive strength of concrete, because the rate of increase in service life is larger than the rate of increase in cost [48].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Strength (MPa)** & **Eco-Efficiency (KRW/year)** & **Cost (KRW/m\({}^{3}\))** & **Service Life (year/m\({}^{3}\))** \\ \hline
21 & 5700 & 308,000 & 54 \\
24 & 4700 & 318,000 & 68 \\
27 & 3800 & 333,000 & 89 \\
30 & 3300 & 347,000 & 110 \\
40 & 2200 & 387,000 & 190 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Eco-efficiency according to compressive strength.
## 4 ABER for SIMO TOW Systems with Receivers' Diversity
### SIMO TOW System with IM/DD L-PPM  
Considering (19) for the \(M\) copies of the optical signal with \(L\)-PPM and OC, the ABER of the total SIMO TOW system can be expressed as  
\[P_{\epsilon,\alpha\
u,M}^{L-PPM}=\int_{\mathrm{h}}f_{\mathrm{h}}(\mathrm{h})\ \ Q \Bigg{[}\sqrt{\frac{\mathrm{L}\log_{2}L}{4\mathrm{M}N_{0}}\sum\limits_{m=1}^{M} \left(\eta_{m}h_{m}\right)^{2}}\Bigg{]}d\mathrm{h}\ \, \tag{28}\]  
Next, by using the accurate enough approximation of [65] for the Q-function, \(Q(x)\approx\frac{e^{-x^{2}/2}+\frac{3}{2}e^{-x^{2}/3}}{12}\), into (28) the latter gives  
\[\begin{array}{ll}P_{\epsilon,\alpha\
u,M}^{L-PPM}&\approx\frac{L}{24}\prod \limits_{m=1}^{M}\int\limits_{0}^{\infty}\exp\biggl{(}-\frac{\eta_{m}^{2}h_{m }^{2}L\log_{2}L}{8\mathrm{M}N_{0}}\biggr{)}f_{h_{m}}(h_{m})dh_{m}\\ &+\frac{L}{8}\prod\limits_{m=1}^{M}\int\limits_{0}^{\infty}\exp\biggl{(}- \frac{\eta_{m}^{2}h_{m}^{2}L\log_{2}L}{6\mathrm{M}N_{0}}\biggr{)}f_{h_{m}}(h_{ m})dh_{m}.\end{array} \tag{29}\]  
Thus, by using \(\int\limits_{0}^{\infty}x^{n}e^{-x^{2}}dx=\frac{\Gamma\left((n+1)/2\right)}{2x \frac{n+1}{2}},n>-1,z>0\)[59], and by substituting (3) in (29), the following ABER expression is derived for a IM/DD \(L\)-PPM SIMO TOW system with OC, under the joint impact of both skin-induced attenuation and NZB pointing errors  
\[\begin{array}{ll}P_{\epsilon,\alpha\
u,M}^{L-PPM}&\approx\frac{L}{24}\prod \limits_{m=1}^{M}\frac{\psi_{m}^{2}\exp(-\alpha(\lambda_{m})\delta_{m}/2)\psi_ {m}^{2}-1\Gamma\left[(1-\psi_{m}^{2})/2\right]}{2\left(\eta_{m}^{2}L\log_{2}L /8\mathrm{M}N_{0}\right)^{\frac{1-\psi_{m}^{2}}{2}}\left(A_{0,m}g_{m}\right) \psi_{m}^{2}}\\ &+\frac{L}{8}\prod\limits_{m=1}^{M}\frac{\psi_{m}^{2}\exp(-\alpha(\lambda_{m} )\delta_{m}/2)\psi_{m}^{2}-1\Gamma\left[(1-\psi_{m}^{2})/2\right]}{2\left(\eta_ {m}^{2}L\log_{2}L/6\mathrm{M}N_{0}\right)^{\frac{1-\psi_{m}^{2}}{2}}\left(A_{ 0,m}g_{m}\right)\psi_{m}^{2}}.\end{array} \tag{30}\]  
Additionally, by substituting (12) into (30) we obtain the latter expression as a function of the average electrical SNR, \(\mu_{m}\)  
\[\begin{array}{ll}P_{\epsilon,\alpha\
u,M}^{L-PPM}&\approx\frac{1}{12}\prod \limits_{m=1}^{M}\frac{\psi_{m}^{3-\psi_{m}^{2}}\left(A_{0,m}g_{m}\right)^{1- 2\psi_{m}^{2}}\left(8\widetilde{P}_{\epsilon}M\right)^{\frac{1-\psi_{m}^{2}}{ 2}}\Gamma\left[(1-\psi_{m}^{2})/2\right]}{2\left[\mu_{m}\left(\psi_{m}^{2}+2 \right)L\log_{2}L\right]^{\frac{1-\psi_{m}^{2}}{2}}}\\ &+\frac{1}{4}\prod\limits_{m=1}^{M}\frac{\psi_{m}^{3-\psi_{m}^{2}}\left(A_{0,m }g_{m}\right)^{1-2\psi_{m}^{2}}\left(\widetilde{\epsilon}\widetilde{P}_{\epsilon }M\right)^{\frac{1-\psi_{m}^{2}}{2}}\Gamma\left[(1-\psi_{m}^{2})/2\right]}{2 \left[\mu_{m}\left(\psi_{m}^{2}+2\right)L\log_{2}L\right]^{\frac{1-\psi_{m}^{2}} {2}}}.\end{array} \tag{31}\]
# Resource Management for Collaborative 5G-NR-V2X RSUs to Enhance V2I/N Link Reliability
## 3 System Model of Cellular V2X Network  
This study implements a 3 GPP general deployment scenario that provides networks, including V2V, V2I/N, and V2P services [16]. The study by [14] presents a methodology for evaluating new features required to operate LTE-based V2X services, and uses it to compare performance when applying various technologies [17]. On the other hand, [15] studies the evaluation methodology of new V2X use cases for LTE and NR, comparing the performance of various technology options for new 5G V2X use cases [18]. Further, [16] investigates NR-V2X to address the improvement of the Uu interface to support improved V2X use in NR-V2X, and the allocation/configuration of Uu interface-based sidelink (SL) resources by LTE and NR [19]. This section covers system setup, including network layout, antenna model, channel model, and physical layer, to achieve requirements in 5G NR environments by applying the simulation methodology of the mentioned 3 GPP documents. In this study, we perform simulations using link-to-system (L2S) mapping through the utilization of link-level simulator (LLS) and system-level simulator (SLS) based on MATLAB. We implement our own SLS based on MATLAB using the system model presented in Sections 3.1-3.3; and for LLS, we utilize the Vienna 5G LL Simulator, an open-source simulator based on MATLAB, to model link-level performance according to the 5G LDPC codes design approach described in Section 3.4, and apply it to the system.
## 3 Problem Formulation
### Server Power Model  
Each server in the system has a different capacity to host VMs based on its configuration and the sizes of the VMs. The power consumption of a server is influenced by the incoming load it receives, which can vary over time depending on the scheduling policy. The relationship between server power consumption and _CPU_ utilization can exhibit different patterns, such as being constant, cubic, or quadratic [29]. The goal of energy-efficient server design is to achieve energy proportionality, where servers consume power only when there is a workload present. In contemporary servers, the idle power consumption (\(PM_{i,min}^{Power}\)) is typically half of the peak power consumption (\(PM_{i,max}^{Power}\)). In this work, we establish a model for the relationship between server power consumption and server utilization using data derived from the SpecPower benchmark [3]. The benchmark data reveals that as server utilization increases, the total power drawn by the server follows a linear growth pattern. Consequently, in this paper, we directly link server utilization to _CPU_ utilization as in Equation (5), recognizing their close relationship.  
\[PM_{i,t}^{Power}=PM_{i,min}^{Power}+\left(PM_{i,max}^{Power}-PM_{i,min}^{Power} \right)\times PM_{i,current}^{CPU} \tag{5}\]
## 1 Introduction
Combing signals from multiple transmitters, the coordinate of the receiver can be obtained by the  
Figure 1: Transmitter configuration. optimization algorithm [17]. At present, the research on static measurement performance of wMPS has been carried out and it gains an accuracy of 0.2 mm + 0.01 mm/m at a frequency of 20 Hz. However, with the development of industrial automation, dynamic positioning has been increasingly required. In aircraft and ship assembly, large components are carried by industrial robots for docking. The real-time positions of components are necessary for feedback control. Besides, the relative position between different robots is also important to avoid the deformation caused by measurement asynchronization [18; 19]. For indoor autonomous guided vehicle (AGV), accurate positioning in global positioning system (GPS) denied conditions is the foundation for navigation and control. On the one hand, wMPS has the potential for dynamic measurement, fast dynamic response, and good static stability. On the other hand, limited by the principle of angle intersection measurement, the error is inherent and non-negligible when the receiver is in motion. Dynamic error analysis and compensation has been the key to further improve the system performance and extend the application area. In this article, we first introduce the dynamic error causes of wMPS. Then we construct the error model, discuss the error characteristics, and quantize the measurement uncertainty at different cases through simulation experiments. Finally, practical experiments are carried out to validate the proposed model.
## 4 Applications of Fe\({}_{3}\)O\({}_{4}\) Nanoparticles  
### Biomedical Applications (Therapeutic and Diagnostic Technologies)  
Combinations of excellent magnetic properties, inexpensive materials, great biocompatibility, and iron oxide have been widely investigated in various medical applications [35]. FDA approval for employing iron oxide nanoparticles as medical contrast agents for magnetic resonance imaging (MRI) indicates significant progress in applying magnetic iron oxide nanoparticles. Conventionally, magnetic contrast agents, hyperthermia, and drug delivery are the three most frequently explored biomedical applications of Fe\({}_{3}\)O\({}_{4}\) nanoparticles. Furthermore, surface functionalization or preparation of bioconjugated Fe\({}_{3}\)O\({}_{4}\)-based substances has provided additional applications. We will introduce applications of Fe\({}_{3}\)O\({}_{4}\) nanoparticles and their functionalized or core-shell structures in MRI,hyperthermia, and drug delivery. Moreover, we will highlight recent efforts in the preparation of multifunctional nanoparticles that integrate multiple applications in a single Fe\({}_{3}\)O\({}_{4}\) nanoparticle-based system.
## 4 Results
### Subjective Results
#### 4.1.5 Bedford Workload Scale  
We expanded the analysis of participants' cognitive ergonomics by obtaining their spare mental capacity through the Bedford Workload Scale. The results for this index are presented in Figure 5 accompanied by NASA-TLX's overall workload scores.  
Statistical significance was identified in our second measurement technique (F (3, 48) = 6.41, \(p\) = 0.001, \(\eta\) = 0.29), in which higher amounts of spare mental capacity were obtained in scenarios with humans holding the leader role. The latter can be seen in Scenario I with Scenarios III (T = 3.16, \(p\) = 0.016) and IV (T = 2.57, \(p\) = 0.081) in and Scenario II with Scenarios III (T = 3.55, \(p\) = 0.005) and IV (T = 2.96, \(p\) = 0.029). These Bonferroni corrections were all significant except for the Scenario I-IV test, which differs only marginally.  
Figure 4: Results of NASA-TLX’s workload factors by collaborative scenario.
# Domain Adaptation for Satellite-Borne Multispectral Cloud Detection
## 3 Domain Adaptation Tasks for EO Mission
### Post-Deployment Domain Adaptation
#### 3.3.1 Offline Adaptation  
Offline adaptation assumes the ability to downlink \(\hat{\mathcal{D}}^{t}\). We can thus label \(\hat{\mathcal{D}}^{t}\) (e.g., via manual labelling) and form the **labelled target dataset**\(\mathcal{D}^{t}=\{x_{j}^{t},y_{j}^{t}\}_{j=1}^{N^{t}}\). Details on building \(\mathcal{D}^{t}\) are provided in Section 4. A straightforward approach to domain adaptation is to update \(f^{s}\) on \(\mathcal{D}^{t}\) to obtain \(\theta^{t}\), which amounts to conducting SDA (see Section 2.2). Then, \(f^{d}\) is updated by uplinking \(\theta^{t}\) to remotely replace \(\theta^{s}\). The steps from downlinking \(\hat{\mathcal{D}}^{t}\) to uplinking \(\theta^{t}\) are depicted in Figure 3.  
However, the ability to downlink \(\hat{\mathcal{D}}^{t}\) does not imply the ability to uplink \(\theta\), particularly if the architecture of \(f\) is complex; for example, ResNet50 with \(\approx\)23 M single-precision floating-point (FP32) weights has a memory footprint of 94.37 MB. This is because many satellite communication bandwidths are asymmetrical [73], in that larger bandwidths are allocated for downlinking to support large-volume telemetry, while much smaller bandwidths are allocated for uplinking since telecommand traffic is sparser. Indeed, PhiSat  
Figure 2: Pre-deployment model training involves supervised training using labelled source data from a ground station equipped with a GPU-based computer. Once trained, the model is deployed onto the satellite and launched into orbit.  
1 restricts the maximum memory footprint of \(f\) to 5 MB to permit the model to be remotely updated [5]. However, this limits the learning capacity of \(f\), which could reduce its accuracy and increase its susceptibility to adversarial attacks [37].  
To alleviate the uplink restrictions on model size, it is vital to perform SDA in a bandwidth-efficient manner. This can be achieved by restricting the number of individual weights of \(\theta^{s}\) that are changed when updating \(f^{g}\) on \(\mathcal{D}^{t}\). Then, only a small number of refined weights are uplinked to remotely update \(f^{d}\). The problem is summarised as follows:  
\begin{tabular}{l} \hline \hline Problem 1: Bandwidth-efficient SDA \\ \hline Given labelled target dataset \(\mathcal{D}^{t}=\left\{x_{j^{\prime}}^{t},y_{j}^{t}\right\}_{j=1}^{N^{t}}\) and source cloud detector \(f^{g}(\cdot\,;\,\theta^{s})\), update \(f^{g}\) using \(\mathcal{D}^{t}\) by making as few changes to the source weights \(\theta^{s}\) as possible. \\ \hline \hline \end{tabular}  
In Section 6.1, we describe a solution to Problem 1 that enables only a small fraction of the weights to be updated without noticeable impacts to cloud detection accuracy. This enables large models to be used and remotely updated through the thin uplink channel.
## Materials and Methods
### Analytical Figures of Merit of the Method Developed  
River water spiked with five concentrations of each analyte ranging from 0.5 to 25.0 \(\upmu\)g L\({}^{-1}\) was prepared to build calibration curves which were used to calculate the linear coefficient of determination (R\({}^{2}\)). The lowest concentration on the analytical curve for each analyte which enabled measures with acceptable precision (RSD < 20%) was adopted as limits of quantification (LOQs). The limits of detection (LODs) were obtained dividing the LOQ by 3.3. The precision and the accuracy of the method were evaluated by performing extractions using real water samples spiked with the analytes at 0.5 \(\upmu\)g L\({}^{-1}\). Precision was calculated as the relative standard deviation (RSD) obtained from spiked river water and accuracy was verified through the relative recovery of the analytes.
## 2 Materials and Methods
### Data Sets  
In situ data were gathered by the Estonian Marine Institute in the frame of the national monitoring program. The measurements of the chl-a and cyanobacterial biomass were performed according to the HELCOM guidelines (phytoplankton [41] and chl-a [42]) for the period of 2016-2022. Chl-a was measured spectrophotometrically, using extraction in 96% ethanol and calculation according to Arvola, 1981. The in situ sampling points in the various coastal areas are given in Figure 1. The depth of the measurements ranged from 0 m (on the surface) to 10 m under the surface. The temperature measurements were obtained from the Estonian Environment Agency which were taken at various ports across the coastline.  
For satellite data, Sentinel-3A/B OLCI Level-1 full-resolution data were used. For validation, the satellite data were gathered from April to September. At higher latitudes, it is not possible to stably use satellite data due to the sun's angle earlier in the year which disperses incoming solar irradiance onto a larger area, decreasing the energy per unit area. In situ data were used to validate the chl-a of the two processors C2 RCC and POLYMER. POLYMER is an atmospheric correction model based on spectral matching. It uses a polynomial to model the spectral reflectance of the atmosphere, a water reflectance model, and all available spectral bands in the visible spectral region [43]. C2 RCC is a neural-network-based atmospheric correction processor which provides the results of water-leaving reflectance (\(\rho_{\text{w}}\)), inherent optical properties (IOPs), chl-a, and total suspended matter (TSM) [44; 45]. Here, the chl-a outputs (chl_conc product) of C2 RCC were used. The pixel flags are given in Table 2. The monthly temperatures were averaged and, along with a salinity of 5 PSU, were used as input for the Case-2 Regional CoastColour processor (C2 RCC) v1.9.  
\begin{table}
\begin{tabular}{l l} \hline
**Flags Used with C2 RCC** & **Flags Used with POLYMER** \\ \hline IDEPIX\_CLOUD & bitmask = 0 \\ IDEPIX\_BRIGHT & bitmask = 1024 \\ IDEPIX\_CLOUD\_SHADOW & \\ IDEPIX\_CLOUD\_AMBIGUOUS & \\ IDEPIX\_CLOUD\_SURE & \\ IDEPIX\_CLOUD\_BUFFER & \\ Cloud\_risk & quality\_flags.bright \\ quality\_flags.straylight\_risk & \\ quality\_flags.invalid & \\ quality\_flags.sun\_glint\_risk & \\ \hline \end{tabular}
\end{table}
Table 2: The flags used to exclude pixels for C2 RCC and include pixels for POLYMER.  
Validation of the satellite data was performed according to the Sentinel-3 validation recommendations [46]. A minimum number of valid pixels were chosen to be at least 50% for each 3 \(\times\) 3 window. Although not in the recommendations of the S3 VT, in order to consider more data points, a maximum 12 h difference in time was chosen. The pixel outliers were removed according to the mean and standard deviation (SD) of the results following the filtration methods in the recommendations, and the values within a coefficient of variation of 20% were used. The correlation was estimated based on the coefficient of determination and Pearsons's correlation coefficient.