esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of an Energy Efficient Future Base Station with Large-Scale Antenna System
## 2 System Model
### Power Consumption Model
#### 2.1.1 Power Amplifier  
The PA is one of the most power-hungry devices in the current BS. Current cellular standards, i.e., 3 GPP-LTE-A, IEEE 802.16 (e/m), use orthogonal frequency division multiplexing (OFDM) for downlink transmission. The OFDM has a very high peak-to-average power ratio (PAPR), and this significantly reduces the PA efficiency [22]. If we assume a 10-MHz BW with a 1024 IFFtsize, with less than a 1% distortion probability or complementary cumulative distribution function (CCDF), PAPR is known to be higher than 11 dB. Based on this, we choose an input-back-off (IBO) = 11 dB [22]. We also simply assume the use of the maximum efficiency of the Class-B amplifier (78.5%). Then, the PA efficiency, \(\eta(\%)\), can be represented as [23; 24]:  
\[\eta(\%)=\frac{\pi}{4p} \tag{2}\]where the \(p\) is the \(\sqrt{IBO}\). Depending on the parameters that we mentioned, the PA efficiency can be \(\eta=22\%\). However, various advanced technologies can be applied to obtain an increase by more than that, and thus, we reasonably choose \(\eta=25\%\). The simple relation between the transmission power \(P_{tx}\) and \(P_{PA}\) can be represented as:  
\[P_{tx}=\eta P_{PA} \tag{3}\]  
It is possible for the TX power to be reduced if we increase the \(N_{t}\), due to the channel gain increment. Note that we assume the system does not adopt any predistortion and/or PAPR reduction techniques.
# Towards Interactive Analytics over RDF Graphs
## 6 Translation of HIFUN Queries to SPARQL
### Complex Grouping Queries
#### 6.4.1 Composition  
Suppose that we ask for the total quantities of products delivered by brand. This query would be expressed in HIFUN as (_brand_\(\circ\)_delivers, inQuantity, SUM_), and in SPARQL as:  
```
SELECT?x3 SUM(?x4) WHERE{?x1 ex:delivers?x2.?x2 ex:brand?x3.?x1 ex:inQuantity?x4. } GROUPBY?x3
```  
Therefore, a HIFUN query (\(f_{k}\circ\),..., \(\circ\)\(f_{2}\circ f_{1}\), \(m\), \(op\)) would be translated as follows. At first note that if instead of the composition we had one function \(f_{1}\), then it would be interpreted as a single query, i.e., we would add the triple pattern?x1\(f_{1}\)?x2 to the WHERE clause and the variable right(\(f_{1}\)) would be added to the SELECT and to the GROUP BYclause. If we had the composition of two functions (\(f_{2}\circ f_{1}\)), then we would add the triple patterns?x1 \(f_{1}\)_right_(\(f_{1}\)) and _right_(\(f_{1}\)) \(f_{2}\)?x2r (where xf2r is a brand new variable) to the WHERE clause and the variable right(\(f_{2}\)) to the SELECT and to the GROUP BY clause.  
Now suppose that the composition function comprises \(k\) functions, (\(f_{k}\circ\),..., \(\circ\)\(f_{2}\circ f_{1}\)), which would be translated to the triple patterns?x1 \(f_{1}\)_right_(\(f_{1}\)), _right_(\(f_{1}\)) \(f_{2}\)_right_(\(f_{2}\)),..., _right_(\(f_{k-1}\)) \(f_{k}\)_right_(\(f_{k}\)) to the WHERE clause and the variable right(\(f_{k}\)) to the SELECT and to the GROUP BY clause. If we added one more function to the composition (reaching to k+1 functions), i.e., (\(f_{k+1}\circ f_{k}\circ\),..., \(\circ\)\(f_{2}\circ f_{1}\)), we would have to add the triple pattern _right_(\(f_{k}\)) \(f_{k+1}\)?_xnew_ (where?_xnew_ is a brand new variable) to the WHERE clause and to replace the variable right(\(f_{k}\)) with the right(\(f_{k+1}\)) in the SELECT and in the GROUP BY clause.  
Now we shall provide an example of composition with a _derived_ attribute. Suppose that we ask for the total quantities of products delivered by month. This query would be expressed in HIFUN as (_month \(\circ\) date, inQuantity, SUIM_) and in SPARQL as:  
SELECT month(?x2) SUIM(?x3) WHERE { ?x1 ex:hasDate?x2. ?x1 ex:inQuantity?x3.  } GROUP BY month(?x2) ```
Therefore, a HIFUN query \((f\circ g,m,op)\), where the attribute \(f\) derives from \(g\), would be translated by adding to the WHERE clause the triple pattern?x1 \(g\)?x2r (where?x2r is a brand new variable). Then, \(f\) would be derived from _right_(\(g\)) by adding to the SELECT and to the GROUP BY clauses a SPARQL build-in function i.e., _right_(\(g\))) (in our example month(?x2)); this function would extract the value \(f\) from that of _right_(\(g\)).
## 2 Study Area and Data
### Data
#### 2.2.3 Land Surface Temperature Data  
MODIS offers 8-day land surface temperature (LST) products, available for both day and night, with a spatial resolution of 1 km. The MODIS LST-day product (MOD11A2) was downloaded for Henan Province, and the monthly LST results were calculated by the maximum value synthesis method from 2001 to 2020. Similarly, LST data can be obtained from GEE.
## 5 Two Level Solution Method  
The robust programming model built in this paper mainly includes network reconfiguration and \(DG\) programming. Because the two parts are nonlinear programming and have many dimensions, the overall solution is difficult. Therefore, in this paper, the two-level solution method is first used to decompose the model, so as to reduce the dimension of the model and reduce the difficulty of solution.  
The bilevel programming model adopted in this paper is specifically expressed as:  
\[\left\{\begin{array}{l}\min\!F\!\left(x^{Y},x^{DG}\right)=C^{Y}+C^{DG}\\ s.t.G\!\left(x^{Y},x^{DG}\right)\leq 0\\ H\!\left(x^{Y},x^{DG}\right)=0\\ \min\!\left(x^{Y}\right)=C^{Y}\\ s.t.g\!\left(x^{Y}\right)\leq 0\\ h\!\left(x^{Y}\right)=0\end{array}\right. \tag{24}\]  
where \(F(\cdot)\) and \(f(\cdot)\) are the upper and lower objective functions, respectively; \(G(\cdot)\), \(H(\cdot)\), \(g(\cdot)\) and \(h(\cdot)\) are upper and lower layer constraints.  
The upper level model aims to solve the location and capacity of \(DG\), so as to minimize the comprehensive cost. The constraints are related to \(DG\) planning. The lower level model aims to solve the optimal reconfiguration combination of the network, so as to minimize the network operation cost. The constraints are the number of network reconfiguration constraints and network operation related constraints.  
The upper layer and the lower layer are solved separately and interrelated, that is, the upper layer transfers the \(DG\) planning results to the lower layer, and the lower layer transfers the reconstructed topology and operating costs to the upper layer. The overall block diagram of the two-layer model solution method is shown in Figure 4.  
Figure 4: The framework of the two-level solution model.
## 3 Results  
### Damage Severity of the Forest Fire  
NBR\({}_{2.1}\), NBR\({}_{1.6}\), NDVI, and GPP were analyzed to evaluate the damage severity of the East Coast fire. Figure 3 shows the results acquired in May 2000, immediately after the forest fire. The meanof NBR\({}_{2.1}\) values was 0.480 in the reference area, and NBR\({}_{2.1}\) values in areas with low, moderate, and high levels of fire severity gradually decreased and were 0.432, 0.381, and 0.321, respectively (Figure 3a). The values in the areas with low, moderate, and high fire severity decreased by 10.03%, 20.52%, and 33.13%, respectively, in comparison with the reference area values. The mean of NBR\({}_{1.6}\) values was 0.243 in the reference area, which was lower than that of NBR\({}_{2.1}\) (Figure 3b). NBR\({}_{1.6}\) values of the burned area decreased when fire severity increased (low: 0.199; moderate: 0.166; high: 0.163); the same trend was observed for NBR\({}_{2.1}\). NBR\({}_{1.6}\) values were lower than those of NBR\({}_{2.1}\), but the percentage decreases in the low, moderate, and high burned area compared to the reference area were 18.14%, 31.72%, and 32.78%, respectively. These results showed that NBR\({}_{1.6}\) was better at detecting differences between the burned and reference areas than NBR\({}_{2.1}\), particularly for low and moderate severity levels. However, the difference between low and high damage levels was more distinguished with NBR\({}_{2.1}\) than with NBR\({}_{1.6}\).  
Figure 3: Vegetation indices and gross primary production (GPP) based on forest fire severity. Red circles indicate average values, and blue dash lines indicate median values: (**a**) NBR\({}_{2.1}\); (**b**) NBR\({}_{1.6}\); (**c**) NDVI; and (**d**) GPP.  
NDVI values were noticeably different depending on the damage severity of the forest fire (Figure 3c). The decreasing pattern of the NDVI with increase in damage severity was similar to that of the NBR. The mean NDVI values in the reference area was 0.710. The percentage decrease of NDVI values in the burned areas was 9.84%, 24.90%, and 35.56% in low, moderate, and high severity areas, respectively. Change in GPP due to the forest fire showed a steeper decline than the NBR\({}_{2.1}\), NBR\({}_{1.6}\), and NDVI (Figure 3d). The percentage decrease of GPP in areas with low severity levels (13.67%) was not noticeably different compared with other variables, but the percentage decrease in areas with high severity levels (56.17%) was the largest.
## 3 Results and Discussion
### Trade-Offs Between DDL and LCC in a GRE1 Context
Conversely, centralized networks (\(DDL\leq 40\%\)) exhibited larger collectors and deeper manholes, typical of conventional stormwater infrastructure, as illustrated in Figure 3.  
Interestingly, a fully decentralized network (\(DDL=100\%\)) and a network with \(DDL=80\%\), when designed with the same manhole depth, showed only marginal fiscal benefits from reduced pipe diameters, yielding a modest 0.7% reduction in total LCC. This observation underscores the intricate balance in GREI design and highlights the nuanced economic feasibility of decentralized stormwater management solutions [46]. These findings emphasize the importance of a comprehensive evaluation framework for optimizing the cost and performance of GREI systems across varying levels of decentralization.
## 5 Applications of Activated Carbon from Waste Tyres  
AC has many direct or indirect applications in the medical industry, due to its exceptional level of purity coupled with adsorptive features. In 1830, ACs were successfully used as a remedy for treating poisons [50], resorption of agents involved in enterogastric cycling, and gastrointestinal dialysis [51]. In the blood dialysis process, AC has been used as a filtering medium and acts as a sorbent that eliminates the waste products such as urea, indoxyl sulphate, and other toxic radicals [52]. In wastewater treatment, AC has been employed to improve the quality of water by removing excess chlorine, and substances causing odour and taste. Due to water scarcity, overpopulation, and high pollution levels all over the world, it is crucial to treat the water using a harmless and economically viable method to meet the water quality requirements [53].  
Rechargeable batteries and supercapacitors are composed of electrodes whose performance depends on the properties of an electrode material such as conductivity, surface area, and porosity. Various electrode materials such as metal oxides, metal hydroxides, and conducting polymers have been explored for a high-energy performance. Recently, lithium-ion batteries and supercapacitors have used biowaste to derive hardcore carbon as electrode materials, due to the limited resource of fossil fuels, and the need to search for an alternative material to satisfy the future need. Carbon materials such as activated carbon, carbon black, graphene, and carbon aerogels have been used as electrode materials [54; 55; 56; 57].  
The utilization of waste tyre, bio waste, polymers, and coal-based AC as electrode materials make the process easier, cheaper, and eco-friendly. AC can either serve as a sole electrode or can be tunable by attaching it to the heteroatoms to improve the electrochemical performance. As mentioned in the previous sections, the quality and property can be improved through pyrolysis and activation processes [58; 59].  
Similar to other carbon adsorbents, waste tyre-derived AC has also been employed in the last two decades for various applications in the field of wastewater treatment, dye removal, and energy storage. Important properties such as porosity, surface area, adsorption capacity, low cost, and eco-friendliness have led to the use of AC derived from waste tyres as a suitable material for various applications. A few applications of waste tyre-derived AC are presented in the following sub-sections. Among various applications of AC, since this review focuses on wastewater treatment and water purification for the removal of different pollutants from water, this application is reviewed in more detail in the next subsection.
## 2 TCAPLL Architecture  
The TCAPLL architecture is shown in Figure 1 where the four main blocks of the architecture can be identified as the GPS, the INS the integration filter and the data monitoring system. The measurement processing starts by running the GPS receiver alone and, once the ephemeris and a position, velocity and time (PVT) solution are obtained, the INS can be initialized and synchronized with the GPS time. Next, the position, velocity and attitude (PVA) solution from INS is projected into LOS to obtain INS ranges and Doppler frequencies which are combined with the raw pseudoranges and Doppler measurements from the GPS tracking loops to form the input of the centralized integration filter. The filter directly accepts their differences to compute the error corrections which are used to update the final position, velocity, attitude and time (PVAT) solution and the INS predicted errors, _i.e_., bias drifts and scale factors. The Doppler frequency predicted by the INS is added to the GPS oscillator frequency drift estimated by the integration filter to form a feed-forward component to assist the GPS PLL tracking loops. Note that the predicted INS Doppler frequency is taken directly from the output of the LOS projection block instead of the integration filter. The reason for this is that the integration filter update frequency is much lower than the INS prediction frequency. Therefore, to keep the GPS tracking loops up to date with the latest Doppler frequency, the INS output is taken and the corresponding INS Doppler is corrected every time the integration filter is updated. As a result, because the PLL tracking loops have an external aiding frequency, the dynamics and the receiver oscillator clock errors do not need to be tracked any more by the PLL loop filter and only the PLL thermal noise and the error of the external aiding component should be accounted for.  
As we intend to use low-quality MEMS inertial sensors, which are characterized by high integration drifts, the measurements from the INS have to be continuously monitored in order to validate the computed navigation solution, and to select when to enable and disable the feed-forward component. This timing depends on two factors: the INS sensor quality and the integration filter. Therefore, a monitoring system responsible for checking the quality of the INS measurements is implemented as part of the TCAPLL architecture in order to ensure robust tracking and accurate navigation. In addition, the data monitoring system observes the GPS tracking loops to check the visibility of the tracked satellites and to validate their corresponding raw measurements. The design of each of the TCAPLL blocks is described in the following sections, but first the model of the errors of the MEMS-based IMU used in the evaluation of the TCAPLL is presented.
## 1 Introduction
However, they only took the average CE as the performance indicator and did not analyze its distribution [24]. [PERSON] and [PERSON] analyzed the CE in the condition of an optical system aberration and fiber positioning error. They only considered the influence of the effects caused by turbulence and did not discuss the randomness of the CE due to turbulence [25]. As shown in Table 1, all the researchers do not derive an analytical expression of the CE effected by atmospheric turbulence, and the statistical characteristics are not discussed. In this study, we created a new statistical model of the CE based on non-Kolmogorov turbulence and derived the analytical expression of the PDF. The spectral power law was introduced as a variable to correct the variance of the wavefront error, so that the PDF could fit the actual distribution. The foundation is to evaluate system performance using a statistical method, whether the parameter is linear to the CE or not. We also simulated the model in different conditions. Compared with the model based on the Kolmogorov model, the results showed its rationality and robustness. In addition, an experiment was designed to collect the CE data affected by actual turbulence. We counted the distribution and compared it with the model we built. The results showed that the model based on non-Kolmogorov turbulence could describe the experimental distribution well in all cases. We provide the modeling progress in Section 2, in which the calculation of variance of the wavefront error and the detailed derivation of the PDF are listed. The simulation \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Ref.** & [19] & [20] & [21] & [22] & [23] & [24] & [25] \\ \hline \multirow{3}{*}{Study type} & Theory & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\  & Simulation & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\  & Experiment & - & - & - & - & - & \(\surd\) & - \\ \hline \multirow{2}{*}{Turbulence model} & Kolmogorov & - & - & - & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\  & Non-Kolmogorov & \(\surd\) & \(\surd\) & \(\surd\) & - & - & - & - \\ \hline \multirow{3}{*}{Result form} & Analytical expression & - & - & - & - & - & - & - \\  & Complex integral & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ \cline{1-1}  & Statistical distribution of CE & - & - & - & - & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Study comparison. analysis is presented in Section 3, and the experimental verification with its analysis are shown in Section 4. Section 5 is the conclusion of this study.
## 5 Simulation Results and Analysis
### Simulation Results and Discussions
HE efficiency typically comprises RE source efficiency as well as the energy transfer efficiency, which takes into account the line losses; therefore, in the high efficiency region, the energy transferred through the line with little loss yields a higher optimisation margin for EE. In the following figure, we demonstrate the effects of switching off all the SBSs so that the users are only served by the MBS. Figure 10 displays the impact of MBS HE on the EE performance for various number of UEs per cell in the absence of SBSs. In general, the EE of the system rises 5% on average as the MBS HE is increased. As MBS becomes the sole access point for all of the users, it requires more energy to serve the users. Thus, more HE aids in reducing the GPC and hence leading to a better EE. Furthermore, adding more users per cell increases the EE of the system about 5% on average. This proves that the MBS can still handle the increasing number of UEs with the aids of HE. Figure 8: EE for increasing number of UEs per cell analysed for different BS-Sw methods and BS coordination techniques with \(E_{M}^{HC}=10\) dB and \(E_{S}^{HC}=2\) dB. Figure 9: Impact of the efficiency of HE on the EE of the system analysed for diverse BS-Sw methods with \(E_{M}^{HC}=10\) dB and \(E_{S}^{HC}=2\) dB. As some of the analyses above suggest that the total sum rate rises at a higher rate compared to the total power consumed, further investigation was carried out to prove the hypothesis. While the simulation conditions used for results in Figure 10 were adopted for this analysis, the inference is still applicable for other conditions as well. Figure 11 illustrates the increase rate (for power consumption, the graph trend is not increasing as sum rate, it fluctuates, but for ease of readability, 'increase rate' is used instead of 'variation rate') of the sum rate and total power consumption as the MBS HE is varied. On average, the increased rate of total power consumed is around 5% whereas for sum rate, rate increased by about 20%. Moreover, the sum rate increases almost steadily while the total power consumed fluctuates at certain points as the system regulates the power to maintain a high EE; therefore, it can be inferred that an increase in UEs/cells or the number of SBSs leads to a higher EE. Figure 12 shows the analysis of convergence behaviour of [PERSON]'s algorithm as suggested in theory. On average, the convergence is achieved within three to five iterations for all the methods that were analysed. According to the analysis, it is proven that [PERSON]'s algorithm possesses a fast converging characteristic, which is why it is preferred to be utilised in such complex non-factional problem. Figure 11: Sum rate and total power consumption for various MBS HE with UEs/cell = 5 and all SBSs are in off condition. Figure 10: Effects of the MBS as sole access point on EE for various MBS HE and number of UEs per cell.
# Testing Multi-Frequency Low-Cost GNSS Receivers for Geodetic Monitoring Purposes
## 1 Introduction
### Overview
When relative calibration parameters for antennae were applied, the results indicated that the ZTD was estimated with a comparable accuracy with results obtained from the geodetic antennas. To date, mostly single-frequency low-cost receivers were used for displacement detection or geodetic monitoring, while in this study we tested the potential of multi-frequency low-cost receivers for displacements detection which proved satisfactory performance in open sky.
## 2 Materials and Methods
### Evaluation Procedures
#### 2.4.1 Continuous Evaluation Statistics  
The continuous statistical measures used in this study are the Pearson correlation coefficient (\(COR\)), the bias (\(BIAS\)), the relative bias (\(RBIAS\)) and the root-mean-square error (\(RMSE\)) shown in Equations (1)-(4) respectively. These measures have been utilized before in evaluation studies of precipitation [3; 17; 18] and are described in detail in [PERSON] et al. [19].  
The \(COR\) ranges from \(-1\) to \(1\). Zero values mean there is no correlation between the estimated and observed fields, while non-zero values indicate a linear relationship between the fields. In the case of the \(BIAS\), zero is the desirable value, which means there is no bias between estimated and observed fields, whereas positive (negative) values indicate that the estimated field overestimates (underestimates) the observations. The \(RBIAS\) describes the systematic bias of satellite precipitation and behaves the same as bias. Meanwhile, the \(RMSE\) function emphasizes extremes and does not give the direction of the deviations.  
\[COR=\frac{\sum_{i=1}^{N}(E_{i}-\overline{E})*\left(O_{i}-\overline{O}\right)}{\sqrt{ \sum_{i=1}^{N}\left(E_{i}-\overline{E}\right)^{2}}*\sqrt{\sum_{i=1}^{N}\left(O_ {i}-\overline{O}\right)^{2}}} \tag{1}\]  
\[BIAS=\frac{1}{N}\sum_{i=1}^{N}(E_{i}-O_{i}) \tag{2}\]  
\[RBIAS=\frac{\sum_{i=1}^{N}(E_{i}-O_{i})}{\sum_{i=1}^{N}O_{i}}*100 \tag{3}\]  
\[RMSE=\sqrt{\frac{1}{N}\sum_{i=1}^{N}(E_{i}-O_{i})^{2}} \tag{4}\]  
In the equations, \(E\) stands for the Estimated or modeled values, and \(O\) stands for Observations. \(N\) is the length of the estimated and observed fields. The variables with bars on top denote the mean values of the corresponding field.