esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Theoretical Basis of Tightly-Coupled INS/RTK Integrations
### Observation Model  
Involving the new GNSS observations, this subsection discusses the definition of the observation model. At each GNSS epoch, a new observation \(\mathbf{z}_{k}\) is given by:  
\[\mathbf{z}_{k}=\mathbf{h}(\mathbf{x}_{k})+\mathbf{m}_{k},k=0,1,\ldots,K \tag{10}\]  
The function \(\mathbf{h}0\) presents the observation model. \(\mathbf{m}_{k}\) is the measurement noise whose distribution is independent of not only time but also the system noise. In the positioning process, the observation vector \(\mathbf{z}_{k}\) usually contains a dual-frequency carrier phase as well as a code pseudorange, and eliminates the noise sources that are highly correlated over space by the double-differencing method, as given by Equation (11).  
\[\mathbf{z}_{k}=\left[\begin{array}{c}\
abla\Delta\mathbf{\phi}_{rb}^{st,L1}\\ \
abla\Delta\mathbf{\phi}_{rb}^{st,L2}\\ \
abla\Delta\mathbf{\phi}_{rb}^{st,L1}\\ \
abla\Delta\mathbf{\phi}_{rb}^{st,L2}\end{array}\right]=\left[\begin{array}{c} \frac{1}{\lambda_{1}}\left(\
abla\Delta\vec{\mathbf{r}}_{rb}^{st}\right)+\
abla \Delta\vec{\mathbf{N}}_{rb}+\varepsilon_{\varphi,L1}\\ \frac{1}{\lambda_{2}}\left(\
abla\Delta\vec{\mathbf{r}}_{rb}^{st}\right)+\
abla \Delta\vec{\mathbf{N}}_{rb}+\varepsilon_{\varphi,L2}\\ \
abla\Delta\vec{\mathbf{r}}_{rb}^{st}+\varepsilon_{\varphi,L1}\\ \
abla\Delta\vec{\mathbf{r}}_{rb}^{st}+\varepsilon_{\varphi,L2}\end{array}\right] \tag{11}\]  
Here, \(\
abla\Delta\mathbf{\phi}_{rb}^{st,L1}\) represents the double-differenced carrier-phase vector at \(L1\) frequency with a selected reference satellite \(t\). Analogously, \(\
abla\Delta\mathbf{\rho}_{rb}^{st,L1}\) is the double-differenced pseudorange vector, and \(\
abla\Delta\vec{\mathbf{N}}_{rb}\) and \(\
abla\Delta\vec{\mathbf{r}}_{rb}^{st}\) denote double-differenced integer ambiguities and distances, respectively. \(\
abla\Delta\mathbf{\phi}_{rb}^{st,L2}\), \(\
abla\Delta\mathbf{\rho}_{rb}^{st,L2}\), and \(\
abla\Delta\vec{\mathbf{N}}_{rb}\) are the corresponding double-differenced carrier phase, pseudorange, and integer ambiguities at \(L2\) frequency, respectively. \(\mathbf{\varepsilon}\) is the measurement noise of the relevant observation.  
Taking into account the clock bias between GPS and BDS, the reference satellite \(t\) of each constellation needs to be determined separately. Satellites at lower elevation angles are more likely to be blocked by surrounding buildings or interfered by larger atmospheric errors. To alleviate the hand-over problem, we choose the satellite with the highest elevation angle as a reference satellite.  
It should be noted that the conventional tightly coupled RTK/INS algorithms often use both carrier-phase and pseudorange measurements as the observation vector when there is no fixed solution. However, the new algorithm proposed in this paper, in order to avoid the negative effects caused by large pseudorange errors, only uses the first two items of Equation (11), i.e., double-differenced carrier phases, to update state vectors.
# UAV-Assisted Caching Strategy Based on Content Cache Pricing in Vehicular Networks
## 3 System Model
### Content Request Model  
Assume that there is a total of \(I\) contents, each of equal size, all of \(s\). The set of contents is denoted as \(F=\left\{F_{1},\cdots,F_{i},\cdots,F_{i}\right\}\). Due to the unstable topology of the in-vehicle network, the vehicle user transmission link may be interrupted, resulting in content acquisition failure. In order to ensure the success rate of content transmission, this paper divides the content into a number of equal-sized content blocks and, if the communication link is broken during transmission, the content blocks that have not been transmitted will be discarded and need to be transmitted again after the link is re-established.  
The probability of a file being requested is positively correlated with the file popularity ranking; the probability of the file with the highest popularity ranking being requested can be expressed as [20]  
\[PR_{\tau}=\frac{\tau^{-\gamma}}{\sum\limits_{f=1}^{F}f^{-\gamma}},\tau\in[1,F] \tag{1}\]  
where \(\gamma\) denotes the exponential constant of the Zipf distribution; the larger \(\gamma\) is, the more the user's requests are concentrated in the top ranked files in terms of popularity; \(\tau\) denotes the ranking of the files in the file library. It is assumed that each vehicle user will request content according to popularity before driving off the first road; the request time follows a uniform distribution. The content request indicator variable is denoted by \(re_{n,i}^{t}\), and \(re_{n,i}^{t}=1\) indicates that \(v_{n}\) has requested \(f_{i}\) at the moment \(t\), otherwise \(re_{n,i}^{t}=0\).
# A Novel Multi-User Codebook Design for 5G in 3D-MIMO Heterogeneous Networks
###### Abstract  
The 2D precoding technology can only adjust the beam in a horizontal direction through data processing, which will cause serious problems for multiuser systems, especially at the edge of the base station it will cause serious inter-cell interference. To solve this problem, in the frequency-division duplex (FDD) 3D-MIMO Heterogeneous network system, the influence of feedback overhead on system performance under limited feedback mechanism is studied using random geometry. Based on the deployment of a uniform planar array (UPA) at the base station, a 3D-MIMO multiuser codebook design scheme based on horizontal transmission angle and the vertical down-tilt angle is proposed, and the codebook design scheme is simulated and analyzed. The results show that the feedback overhead and the micro base station density affect the system throughput and even affect the bit error rate (BER) of the 3D precoding scheme. Compared with the precoding scheme based on 2D and 3D discrete Fourier transform (DFT) codebooks, this scheme greatly reduces the system's BER, improves the system's throughput, and optimizes system performance.  
5G; MMSE channel estimation; pilot; precoding; Rayleigh entropy Article
## 3 Data Sets and Methods  
As a powerful tool that efficiently assesses potential natural hazards involving ground deformation, InSAR has been widely employed for seismic activity monitoring [33; 34; 35], land subsidence [35; 36], and landslides [37; 38; 39]. In theory, InSAR is capable of tracing surface deformation up to a few millimeters in ideal error free conditions [40]; however, phase delay components together with externally induced errors such as base DEM and orbital information pose obstacles for precise measurement of surface deformation [41]; thus, the compensation of Atmospheric Phase Screen (APS) is significantly important to ensure precise InSAR measurements. The precision required for our study is a maximum of few tens of millimeters because the deformation over the target area is not a consequence of major earthquake or well confined ground subsidence. To correctly assess the hazards in the target area, we employed rigorous InSAR time series analyses and fused the outcomes with GPS, geophysical modelling, and GIS data.
## 3 Results and Discussion
### Feature Selection Performance Analysis  
The Pearson correlation coefficient can reflect the degree of correlation between two characteristic variables and is defined as the quotient of covariance between variables and their standard deviation product. The closer the absolute value of the coefficient is to \(1\), the stronger the correlation between the two variables is. The closer the absolute value of the coefficient is to \(0\), the weaker the correlation is. Here, in order to verify the superiority of the mRMR selection method, the Pearson correlation coefficient has been employed for comparison.  
\[r=\frac{Cov(X,Y)}{\sqrt{D(X)}\cdot\sqrt{D(Y)}} \tag{23}\]  
\(X\) and \(Y\) in (23) are two individual characteristic parameters, \(\sqrt{D(\cdot)}\) is the standard deviation, and \(Cov(X,Y)\) is the corresponding covariance. The correlation between each two of the first eighteen characteristic parameters in this paper can be obtained by calculation according to (23), and its correlation analysis heat map is shown in Figure 11.  
Obviously, there existed considerable correlations in some parameters, including he near-end amplitude and the amplitude difference, the near-end energy and the energy difference, the far-end duration, and the far-end energy, et al. So, we also increased the optimal selected features from the minimum to maximum and recorded the subsequent identification accuracy, as shown in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline
**Feature Selection Method** & **Feature Dimension** & **Accuracy (\%)** & **Kappa** \\ \hline Pearson & 11 & 83.3 & 0.7435 \\ mRMR & 9 & 92.3 & 0.8624 \\ \hline \end{tabular}
\end{table}
Table 2: The SVM results of Pearson and mRMR feature selection.  
Figure 11: Correlation analysis heat map.  
The results in Table 2 show that the 11-dimensional features selected by Pearson's correlation coefficient method obtained the best identification effect, although its accuracy rate is only 83.3% and the Kappa coefficient is 0.7435. By using the features of the mRMR choosing procedure, the accuracy was improved to 92.3%, and the Kappa coefficient was improved to 0.8624. Compared with Pearson's method, the feature dimension of the mRMR algorithm is lower and the recognition accuracy is higher, which proves its efficiency and effectiveness in the feature selection part of the designed system.
## 1 Introduction
At a 10% CO\({}_{2}\) concentration the average lipid level was about 30% of the total mass, which is a promising indicator for the further processing of this biomass into biodiesel. Therefore, the analysis of the studied works shows the efficiency of microalgae use as guides for atmospheric CO\({}_{2}\), fixing and also the necessity for the further studying of biomass cultivation methods under stress conditions to obtain biofuel. The microalgae biomass extracted after cultivation can be processed in various ways depending on the type of biofuel required. Therefore, biochemical methods for biomass processing are mainly used to produce bio-alcohol (ethanol/butanol) or biogas (methane/hydrogen) [20]. Other types of biofuel, such as biodiesel or bio-oil, can be separated from microalgae chemically and thermally. The traditional method of biomass processing into biodiesel is to extract its lipid part with further intereesterification by various alcohols [21]. However, the peculiarities of microalgae biomass, such as a high moisture concentration (80-90%), make this method of biofuel production economically and energetically unprofitable, as water presence in the lipid part of the biomass reduces the efficiency of intereesterification and, moreover, the use of pre-drying requires more energy than can be obtained after biodiesel combustion [22]. The high moisture concentration in the biomass also makes such methods as pyrolysis and gasification ineffective from the point of energy production [23]. Currently, in connection with this fact, the use of hydrothermal technologies, in particular hydrothermal liquefaction (HTL), has attracted the greatest interest for the production of bio-oil from microalgae [24]. When using this method, the method of biomass processing occurs at a near critical state for the solvent and, consequently, at a high pressure. In the case of microalgae, water is often used as a solvent and the temperature varies from 250 to 350 \({}^{\circ}\)C for biomass production [25]. This method of biomass processing makes it possible to forego pre-drying and gives the opportunity to make the process of bio-oil production uninterrupted. The bio-oil produced by HTL has a higher calorific value than the crude biomass and bio-oil obtained as a result of pyrolysis, which is also an undoubted advantage of this method [26]. Due to the fact that microalgae are very promising source of bio-oil, in this article we decided to describe the whole process for _Arthospira platensis_ microalgae--from the growth under stressful conditions with the increased CO\({}_{2}\) concentration to the transformation of this biomass into bio-oil--and to study its properties. The main targets of this work:  
* Analysis of CO\({}_{2}\) sequestration efficiency by the method of CO\({}_{2}\) absorption by _Arthospira platensis_ and biomass conversion into bio-oil;
* Analysis of bio-oil properties.
## 3 Hardware Design
### Mixed-Signal Processing Module
Besides the two AFEs and the SmartFusion SoC, the mixed-signal processing module is equipped with Ethernet and USB controllers. The Ethernet connection is primarily for instrumentation, in-application reprogramming and debugging in large-scale WSN deployments, where USB topologies  
Figure 2: Wake-up time and current draw comparison of SRAM (**left**) and flash (**right**) FPGAs measured with development kits that utilize similar size SRAM and flash FPGAs. do not scale well. The USB interface, on the other hand, offers a data path to stream raw or partially processed 16-bit I/Q baseband samples to a desktop PC at rates up to 5 MHz. Direct processing of this stream results in a setup similar to USRP/GNU Radio [8], while the recorded stream may aid the development of signal processing blocks and the entire network stack. Therefore, the FPGA and software blocks can be developed based on real-world baseband data acquired through the Marmote SDR radio front-end.
## 2 Data and Methodology
### UCAR RO Data, STAR-Processed Spire Data, Global Radiosonde Data, and Reanalysis Data
The O-B comparison requires forward modeling to derive the refractivity/bending angle from the atmospheric background temperature, humidity, and pressure profiles. In the forward modeling, refractivity is first calculated from the interpolated pressure, temperature, and water vapor profiles and then converted into the refractive index \(n\) to compute the bending angle using the Abel integral.
## 3 Implementation and Testing of an Indoor GPS System
This setup is demonstrated in Figure 5. By utilizing this arrangement, the system was able to map and track positions within the designated test space, allowing for real-time monitoring and testing of the system's capabilities under controlled conditions. The mobile beacons will be introduced into the designated work perimeter one by one, manually. After each beacon's introduction, the system's reported coordinates will be verified for accuracy, and results will be documented. Figure 5 illustrates the placement of all beacons along with the output of the Marvelmind application. Figure 5a-h are screenshots from the Marvelmind dashboard for different scenarios, including a single robot moving in a straight line in different directions, a curvilinear path for one, two, or three robots, etc. Figure 5: Using three beacons. Figure 7a-h are graphs plotted on the horizontal plane based on the (x, y) coordinates provided by the mobile beacons. These were generated using a Python 3 program and show trajectories that resemble those in Figure 7a-h, though not identical. Figure 6: Marvelmind application tests (**a–h**). As expected, the two types are similar. The first set of images provides a qualitative view of the process, while the second set, based on real-time coordinates, is more precise and quantitatively valuable. The objective of this test was to develop an application that not only displays beacon positions quantitatively and visually, but also provides coordinates in a format easily processed and used for the control of mobile robots and the entire system. Figure 7: Proposed application results (**a**–**h**). Different color plots are for different beacons.
# Assessing the Degree of Sustainability Integration in Canadian Public Sector Procurement
## 4 Key Findings  
In the section below we outline the key findings of the analysis. It is important to note that the findings were relatively consistent across jurisdictions assessed. As such, no one level of government, nor one category, stood out as having deeper and broader integration of sustainability with the associated RFPs.  
Figure 4: Model for assessing the expanse of sustainability integration.  
Figure 5: Environmental considerations for a municipal IT RFP.  
Finding 1: Sustainability Integration into RFPs is Currently Superficial with Limited Integration into the Evaluation Process  
The level of inclusion of sustainability in the evaluation process was mostly superficial (see Figure 6). Environmental and or social considerations were often a minor component or omitted entirely within the process. A total of 78% of the RFPs reviewed fell under the first tier and 22% of the RFPs had absolutely no mention of sustainability whatsoever.  
Only 42% of RFPs reached the second tier, somehow integrating sustainability into any component within the evaluation. Again, it is important to note that this tier is still superficial, as integration into the evaluation could take any minimal form. For example, as shown in the municipal IT RFP in Figure 7, sustainability here is focused solely on supplier diversity and integrated in one checklist requiring completion in the proposal. This RFP used a weighted evaluation system, where sustainability was not a separate evaluation category. There is no mention as to what category this checklist falls under and its weight, if any, is unknown.  
Only 12% or six RFPs reached the third tier, integrating sustainability as an independent category within the evaluation section. Those that used a weighted evaluation system had weights ranging from 1% to 15%, with an average of 5% being allocated to sustainability. Even though this was the highest tier any RFPs reached, sustainability integration continued to be minimal, given low weightings and very general language with little indication of specific impact areas.  
Figure 6: Inclusion of sustainability in the evaluation process.  
Figure 7: Inclusion of sustainability within the evaluative component for a municipal IT RFP.  
Although there were two RFPs in total that had a stronger focus on sustainability (with inclusion of sustainability as an independent consideration of 10% or more), they lacked clarity around accountability in the submission process with no mention of mechanisms to ensure compliance.  
In order to provide greater understanding, we included below (see Figures 8-10) samples of the RFPs with the most significant integration from each category.  
Figure 8: Most significant integration example from the construction sector.  
Figure 9: Most significant integration example from the \(\Pi\) sector.
## 2 Materials and Methods
### Finite Element Analysis
#### 2.2.2 Results of the Thermal-Structural Analysis

Table 6: Maximum contact stresses of graphite. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Graphite** & \multicolumn{2}{c}{**SS\({}_{\rm min}\) (66.1\({}^{\circ}\))**} & \multicolumn{2}{c}{**SS\({}_{\rm max}\) (86.1\({}^{\circ}\))**} \\ \cline{2-6}  & **Thickness (mm)** & **22 \({}^{\circ}\)C** & **350 \({}^{\circ}\)C** & **22 \({}^{\circ}\)C** & **350 \({}^{\circ}\)C** \\ \hline \multirow{4}{*}{3-layer} & 0.6 & 2.05 & 169.22 & 1.16 & 129.86 \\  & 0.8 & 2.13 & 154.37 & 1.16 & 128.81 \\  & 1 & 2.14 & 124.61 & 1.51 & 115.68 \\  & 1.2 & 2.26 & 115.37 & 1.60 & 95.33 \\ \hline \multirow{4}{*}{5-layer} & 0.6 & 3.40 & 118.94 & 1.92 & 104.14 \\  & 0.8 & 3.62 & 116.61 & 2.26 & 85.61 \\  & 1 & 3.73 & 113.53 & 2.66 & 79.56 \\  & 1.2 & 3.98 & 104.82 & 2.67 & 76.53 \\ \hline \multirow{4}{*}{7-layer} & 0.6 & 3.46 & 122.55 & 2.71 & 68.67 \\  & 0.8 & 3.66 & 97.15 & 2.99 & 66.81 \\ \cline{1-1}  & 1 & 5.09 & 74.50 & 4.33 & 64.33 \\ \cline{1-1}  & 1.2 & 6.33 & 74.13 & 4.90 & 63.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Maximum contact stresses of stainless steel. (SSmin: 282.17 MPa, SSmax: 199.71 MPa) at 350 \({}^{\circ}\)C, but they were still too large. The 7-layer model with the graphite thickness of 1.2 mm showed the lowest contact stresses of the stainless steel (SSmin: 74.13 MPa, SSmax: 63.02 MPa) at 350 \({}^{\circ}\)C, but the maximum contact stresses of the graphite were below the working pressure (Gmax: 0.79 MPa). Therefore, the 7-layer model with the graphite thickness of 0.8 mm was suggested to guarantee not only airtightness, but also smooth rotation of the disc, and its contact stress distributions are shown in Figures 16 and 17. Analyses results of the new laminated seal at 350 \({}^{\circ}\)C were compared to those at the 1-layer, 3-layer, and 5-layer models in Table 7. The maximum contact stresses in case of the slope angle of 66.1\({}^{\circ}\) were reduced by 65.5%, 42.6%-15.8%, and 18.3%-7.3%, and those in case of the slope angle of 86.1\({}^{\circ}\) were reduced by 66.5%, 48.8%-29.9%, and 35.8%-12.7%, respectively, which means that the shape design of the laminated seal allows to mitigate the excessive contact stress.
## 4 Spatiotemporal Rebalancing Pricing Algorithm
### Optimization Objective  
We can temporarily determine a set of evaluation criteria \(\Phi^{*}=(\phi_{1}^{*},\phi_{2}^{*})\), but there were better criteria initially. The more valuable the evaluation, the better the action guided by the critic produced by the actor module. Therefore, the closer \(Q(S_{t},Ac_{t})\) approaches the real state-action value function \(V(S_{t},Ac_{t})\), the better the evaluation criteria. According to Equation (16) and the actor module, we can obtain the objective function to optimize  
\[Q_{target}(S_{t},Ac_{t})=\mathbb{E}[R(S_{t},A_{t})+\gamma Q^{(}S_{t}+1,Ac_{t}+1| \pi^{*});\Phi^{*})], \tag{19}\]  
where \(R(S_{t},Ac_{t})\) means immediate reward. \(Ac_{t+1}\) is determined on the next state \(S_{t+1}\) under the incentive strategy \(\pi^{*}\) provided by the actor module. The evaluation of next state \(S_{t+1}\) and next action \(Ac_{t+1}\) is computed through the evaluation criteria \(\Phi^{*}=(\phi_{1}^{*},\phi_{2}^{*})\).  
The loss function between the real gain value and the state-action value is constructed. The critic module is optimized through the backward propagation mechanism. The incentive strategy generated under the excellent evaluation criteria is excellent. The loss function is as follows:  
\[Loss_{critic}=MSE(Q(S_{t},Ac_{t}),Q_{target}(S_{t},Ac_{t})), \tag{20}\]  
where MSE stands for mean square error. As mentioned above, the higher the evaluation, the better the action. The loss function of the actor module can be expressed as follows:  
\[Loss_{actor}=-V(S_{t},Ac_{t};\Theta^{*}). \tag{21}\]  
Therefore, the model is divided into four parts: actor, actor-target, critic, and critic-target. The actor-target and critic-target do not have a loss function, and their parameters are regularly duplicated from the actor and critic parts. A relatively smooth soft update method is adopted for the parameters of actor and critic, where \(\tau\) is a hyperparameter.  
\[\bar{\Theta}\leftarrow\tau\Theta+(1-\tau)\bar{\Theta}, \tag{22}\]  
\[\bar{\Phi}\leftarrow\tau\Phi+(1-\tau)\bar{\Phi}. \tag{23}\]