esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Processing of Experimental Data
### Suction and Discharge Temperatures in Relation to Solar Radiation  
The heat pump's compressor back temperature and discharge temperature are crucial indicators affecting the system's performance. With the variation in solar radiation intensity, these two temperatures are significantly influenced, thus holding crucial importance for the overall efficiency and safe operation of the heat pump system. Therefore, this section extensively discusses the impact of solar radiation intensity on the compressor back temperature and discharge temperature throughout the operation of the entire system, as illustrated in Figure 6.  
In the initial phase of system operation, during the self-equilibrium process, the compressor back and discharge temperatures initially increase significantly. Through analysis of Figure 6, this study found that under conditions with an average ambient temperature of 16.5 \(\pm\) 0.5 \({}^{\circ}\)C and average solar radiation intensities of 369, 468, 483, and 510 W\(\cdot\)m\({}^{-2}\), respectively, the average temperature difference between the heat pump compressor discharge and back temperatures ranged from 81.65 to 83.02 \({}^{\circ}\)C, with the discharge temperature reaching \(-11\pm 2\)\({}^{\circ}\)C at the end of the experiment. As solar radiation intensity increases, the back temperature oscillates upwards, with some data showing refrigeration effects and temperatures below 0 \({}^{\circ}\)C.  
Under sunny conditions, the solar radiation intensity exhibits a uniform rise followed by a declining trend, and correspondingly, the back and discharge temperatures generally rise before falling. During the experiment, the compressor discharge temperature fluctuates  
Figure 5: Diurnal Variation Curve of Solar Radiation Intensity and Ambient Temperature in Winter.  
greatly, ranging from 76 to 100 \({}^{\circ}\)C, with a maximum temperature difference of 24 \({}^{\circ}\)C. Throughout the testing period, the average discharge temperature is 89.95 \({}^{\circ}\)C, while the back temperature shows relatively smaller fluctuations compared to the discharge temperature, with a maximum value of 19 \({}^{\circ}\)C, a minimum value of \(-9.5\) \({}^{\circ}\)C, and an average temperature of 7.35 \({}^{\circ}\)C.  
Under overcast conditions, the solar radiation intensity fluctuates synchronously with the back temperature, reaching its maximum value (1093.62 W\(\cdot\)m\({}^{-2}\)) of the day between 10:22 and 12:04 due to the movement of clouds. During this period, the back temperature also oscillates upwards with the fluctuation of solar radiation, while the discharge temperature remains relatively stable. Compared to overcast conditions, the compressor discharge temperature exhibits a greater magnitude of variation, reaching a maximum value of 110 \({}^{\circ}\)C, while the compressor back temperature remains relatively stable, fluctuating between \(-13.5\) and 24 \({}^{\circ}\)C.
## 3 Results
### Comparison between MACS-Countes to FACS Methods  
First, the linear regression plot between CD4\({}^{+}\) T cells counted on Countess\({}^{\text{TM}}\) (Countess results) after MACS separation and the CD4\({}^{+}\) T cells counted by FACS was generated. From that the experimental standard curves (STC) between two methods (\(n=38\)) were yielded and correction factors for the MACS-Countess system were determined (Figure 3). After that, 15 samples with known CD4\({}^{+}\) T cell concentration values from FACS analysis were analyzed on MACS-Countess, and CD4\({}^{+}\) T cell counts were deduced using the correction factor of the STC (Table 1).
## 2 Literature Review
### Infrastructure Management Related Database Based on Machine/Deep Learning Research  
As in Section 2.1 above, most of the studies related to the performance prediction of road pavements used regression. Recently, artificial intelligence (AI) techniques for detecting defects (especially cracks) based on road surface photographs collected through MMS equipment have been introduced. Representative studies include crack detection using deep learning techniques such as convolutional neural network (CNN) and \"You Only Look Once (YOLO)\" [11], future flatness prediction using the support vector machine model [12], and auto portable detection using CNN [13]. There are cases of research using various image-based AI technologies such as fuzzy C-means, holistically nested edge detection, multi-layer perception, random forest classifier, support vector classifier, recurrent neural network (RNN), histogram of oriented gradients (HOG), and grayscale-weighted HOG [14; 15; 16; 17; 18; 19; 20; 21]. Image-based research is also essential for automatic analysis processing; however, there were few cases of introducing the AI method for predicting the road pavement condition index used in decision-making.  
As a result of examining the representative research cases where the AI technique was applied to other facilities, the majority of studies were found to focus on bridges as target facilities. First, since the 1970s, various studies have been conducted to develop a bridge performance prediction model using condition rating data from the national bridge inventory in the United States, primarily using the classification and regression tree and Monte Carlo simulation techniques [23; 24].  
In the 2000s, studies were conducted to predict the future performance and remaining life of bridges using the artificial neural network technique [25; 26; 27]. Furthermore, research on building a life cycle prediction model for a bridge using the long short-term memory (LSTM) algorithm based on bridge inspection history data on national roads has been conducted [28]. LSTM is an updated algorithm of RNN, compensating for the shortcomingsof RNN with a long-term dependency problem that makes it challenging to preserve past data.  
However, as a type of AI machine learning technique, studies using the particle filtering technique, which can be applied to bridges or road pavements that change state in time series, have been conducted [29; 30; 31]. Particle filtering is a technique applicable when predicting non-linear and non-Gaussian distributions, which is known to be applied even when the information on the initial distribution is relatively unclear compared to the existing Bayesian method [32; 33]. Accordingly, this study intends to predict the pavement condition index through data-based research, not image-based research.  
Based on studies in which AI techniques have been applied to various road infrastructures thus far, we used particle filtering, a machine learning technique, in this study. The purpose and application scope of particle filtering have been discussed in Section 3.
## 1 Introduction
In [16], the authors further studied the interference of a fixed-satellite service of GEO system caused by an NGEO satellite; then, a cognitive power control strategy based on range and a traffic-aware strategy were proposed. In [17], the authors proposed a spatial spectrum sharing method by setting protection area to reduce the outage probability of the GEO system. In [18], the authors employed exclusion angles to evade interference; then, downlink performance was analyzed via equivalent power flux density, while in-line interference suppression technologies have been explored for NGEO systems, the avoidance strategy based on uplink and downlink decoupling transmission remains an unaddressed challenge. Figure 1: The system diagrammatic sketch. In this paper, we propose a DUDA method for NGEO MSS that incorporates in-line interference mitigation by modeling spherical surface through geocentric angles to elucidate satellite and UT distribution. Specifically, to maintain the integrity of the angle-based model, the exclusion angle \(\alpha\) delineates the boundary within which NGEO satellites can operate. Given the distinctive exclusion angles for different links within the DUDA methodology, an enhanced number of uplink opportunities exist within the NGEO MSS. To evaluate the performance of the proposed scheme, we utilize stochastic geometry to analysis the association probability and transmission rate as the performance metric. Simulations indicate that DUDA confers a substantial improvement over comparable access schemes.
# A Hybrid Traffic Sensor Deployment Model with Communication Consideration for Highways
## 4 Case Study
### Deployment Plans of the Homogeneous Highways
Since it is beneficial to increase the accuracy rates of monitoring traffic by shortening the space between two adjacent sensors, the interval between the beginning and ending cells will be narrowed when the value of parameter \(\alpha\) is raised. Although the interval in some cases is not significantly reduced, some parts of a deployment plan become more compact. When \(\alpha\) is equal to 0.6, 90 cells are covered from the beginning point to the ending point. Compared with those cases (when \(\alpha\) is less than 0.6), this interval does not become smaller, but sensors installed from cells 2 to 15 are more compact than the average distance. It should be noted that the optimal gap of the solver is set to 0.3 when considering the computation time. This might make some plans depicted in Figure 8 not optimal. In any case, the results reflect that the distance between two adjacent sensors tends to be closer when the objective function pays more attention to improving the total accuracy rate. For the overall consideration of the accuracy rate and the covered regions, \(\alpha=0.5\) is adopted to search for solutions in the following discussions. To further analyze the influence of input factors on deployment plans, Figure 9a shows that the numbers of sensors and servers are changed with a preset budget and the communication radius of an MEC server. It can be seen that the budget and communication radii both have a positive impact on the number of used sensors. Enlarging them will lead to an increase in the quantity of buying sensors. The cause of this phenomenon is that more  
Figure 8: The figure of sensor layout under different weights. money could be transferred to purchase sensors. In Figure 9a, this added investment for sensors is attributed to the total budget increase, and it is obtained by cutting down the investment cost of servers in Figure 9b. Since the communication radius is lengthened, an MEC will have the potential of connecting more sensors, which contributes to reducing the number of installed servers. Therefore, the communication radius has a negative influence on the number of servers in Figure 9b. It can be concluded that USD \(2.1\times 10^{5}\) (CNY 1.5 million) budget is more profitable with these settings, and a communication radius of eight cells is sufficient under a budget of USD \(2.1\times 10^{5}\) (CNY 1.5 million).
# Gaussian-Distributed Spread-Spectrum for Covert Communications
## 5 BER Performance
### LDPC-Coded GDSS System  
This section investigates the use of low density parity check (LDPC) coding to improve the performance for the GDSS scheme. LDPC codes are a type of linear block error correction codes with parity-check matrices (H) that contain a very small number of non-zero entries. The sparseness of H guarantees the minimum Hamming distance and decoding complexity to increase linearly with the code length. Some of the published results show that these codes can perform close to 0.0045 dB away from the Shannon Limit, making it one of the most powerful error-correction codes known today [44]. To reduce the decoding latency a short half-rate LDPC code with number of message bits k = 324 and codeword length n = 648 bits were used for both the GDSS and the traditional DSSS system. The results obtained are presented in Figure 15.  
Figure 13: A selection calculated PDFs of the decision variable \(p_{z}(z)\) and Monte Carlo histograms.  
Figure 15 compares SNR for fixed bandwidth, i.e., the same chip rate in each case. The 1/2-rate coded-CDSS [N = 128] scheme has a power advantage of approximately 5 dB against the uncoded DSSS [N = 256] on the basis of identical information bit rate. However, a comparison of both coded systems (coded-CDSS with coded-DSSS) showed similar performance reduction that was observed for the uncoded system comparison in Section 5.1.  
Figure 14: Uncoded BER performance: theoretical QPSK GDSS (solid lines) and QPSK DSSS (dashed lines) with various spreading factor \(N\), and corresponding simulation results (dots).  
Figure 15: Comparison of LDPC encoded GDSS QPSK system with varying spreading factors against the theoretical DSSS QPSK system’s upper performance bound.
## 3 Materials and Methods
### Estimation of Factorial DOE  
To address this and investigate Mn and Co extraction from ferromanganese crusts, two independent variables were selected for a factorial design comprising 32 experiments: time and the ferromanganese crust/Fe(res) ratio. The experiments were conducted under controlled conditions to evaluate the influence of these variables on extraction efficiency.  
\begin{table}
\begin{tabular}{c c} \hline
**Element** & **Mass (wt. \%)** \\ \hline Fe\({}_{3}\)O\({}_{4}\) & 82.65 \\ Fe\({}_{2}\)O\({}_{3}\) & 13.03 \\ Fe\({}^{0}\) & 0.27 \\ CaO & 0.75 \\ SiO\({}_{2}\) & 1.5 \\ Al\({}_{2}\)O\({}_{3}\) & 0.3 \\ MgO & 0.2 \\ SO\({}_{3}\) & 0.08 \\ P\({}_{2}\)O\({}_{5}\) & 0.2 \\ K\({}_{2}\)O & 0.015 \\ MnO & 0.8 \\ C & 0.2 \\ \hline \end{tabular}
\end{table}
Table 2: Chemical analysis of steel residue.  
The results demonstrated a significant correlation between extraction efficiency and both variables, identifying optimal conditions for maximizing Mn and Co recovery. This methodology enables the evaluation of the most significant variables and their respective impacts while providing an experimental framework that, through coefficient calculation, facilitates the optimization of the response variable [49; 50; 51]. A factorial design was applied involving two factors, each one having three levels; thus, 9 experimental tests were carried out, obtaining the response variables Mn and Co extraction. Minitab 18 software was used for the experimental design and development of a multiple regression equation [52].  
Then, the explained variable was expressed based on linear effect of explanatory variables and considering the effects of interaction and curvature, as shown in Equation (1).  
\[Mn|Co\ Extraction\ (\%)=\alpha+\sum_{\forall i}\beta_{i}x_{i}+\sum_{\forall i} \sum_{\forall j}\beta_{ij}x_{i}x_{j}\,\forall i,j\in\{time,\ crust/Fe\} \tag{1}\]  
where \(\alpha\) is an overall constant, \(x_{i}\) is the value of the level \(i\) of the factor \(x\), \(\beta_{i}\) is the coefficient of the linear factor \(x_{i}\), \(\beta_{ij}\) is the coefficient of the interactions \(x_{i}\cdot x_{j}\), and Mn and Co extraction are the dependent variables.  
Table 3 shows the values of the levels for each factor, while Table 4 shows the recovery obtained for each configuration.
# Towards NGGM: Laser Tracking Instrument for the Next Generation of Gravity Missions
## 2 LTI Design Principle
### LTI Ranging Noise Requirement  
The key science performance requirement for the LTI was the ranging noise in the science measurement band, ranging from 0.1 mHz to 1 Hz. Figure 3 gives an overview of the measured ranging signal of the LRI on GRACE-FO (purple) in comparison to the NGGM LTI and GRACE-FO requirements and performances. The ranging noise requirement of GRACE-FO was depicted in solid blue, the LRI tilt-to length noise term was shown in dashed blue and the cavity frequency noise in black (ground-based measurement before launch).  
Figure 3: NGGM LTI requirements of 40 nm/\(\surd\)Hz (goal: 20 nm/\(\surd\)Hz) multiplied with a noise-shape function \(\sqrt{\left(1+\frac{1\,\mathrm{mHz}}{f}\right)^{2}}\left(1+\frac{10\,\mathrm{ mHz}}{f}\right)^{2}\) in red, with the typical ranging signal in purple. The expected tilt-to-length effect is shown before post-processing corrections in green. The GRACE-FO LRI level 2.5 requirement as the sum of 80 nm/\(\surd\)Hz and 40 nm/\(\surd\)Hz is shown as a solid blue curve, while the tilt-to-length effect as measured in-flight is depicted as a dashed blue curve. The GRACE-FO cavity stability is depicted as a black trace, as derived from pre-flight measurements.  
The NGGM LTI ranging noise requirement and goal and the expected tilt-to-length coupling were drawn in red and green.  
The NGGM LTI had an extended science measurement band requirement down to 0.1 mHz and a stricter ranging noise requirement over the full frequency range. The improved performance was mainly expected to be enabled by a better performance of the satellite platform in terms of the pointing noise, which coupled via tilt-to-length coupling into the ranging noise and an improved thermal noise environment, specifically in the low-frequency region. It was foreseen that the fly would improve the accelerometers on the NGGM to benefit from the improved laser ranging performance, as the accelerometer performance on GRACE FO (and background model errors) was currently limiting the post-fit residuals.  
For a more detailed discussion of the LTI related to the NGGM requirements, the satellite environmental requirements and the LTI performance analysis, please refer to [19]. Below, we focused on a more detailed description of the individual units that formed the LTI.
## 3 Results and Discussion
### Photocatalytic Activity of TiO\({}_{2}\)-Fe\({}_{3}\)O\({}_{4}\) Composite systems
on that observation, the researchers concluded that magnetite does not reduce the energy gap of titania or improve the recombination of e\({}^{-}\)-h\({}^{+}\) holes, as well as reduces the ability of the system to form hydroxyl radicals [26]. A different study reports similar findings in the degradation of rhodamine B [27]. However, in another study, composite films based on TiO\({}_{2}\) and Fe\({}_{2}\)O\({}_{3}\) or Fe\({}_{3}\)O\({}_{4}\) and co-doped with N and C atoms revealed better photocatalytic reduction rates of dichromatic ions by the materials where Fe\({}_{3}\)O\({}_{4}\) was applied [29]. In a study by [PERSON] et al. [31], TiO\({}_{2}\)-Fe\({}_{3}\)O\({}_{4}\) was used for photodegradation of acetaminophen under UV light. Although the achieved degradation rates were high, the TiO\({}_{2}\)-Fe\({}_{3}\)O\({}_{4}\) performed worse than bare TiO\({}_{2}\). Only after co-compositing with SiO\({}_{2}\), the rates approached the ones achieved by bare TiO\({}_{2}\). This only reinforces the notion that for efficient use of Fe\({}_{3}\)O\({}_{4}\) in composite materials, the use of a co-dopant that would stabilize its oxidation-prone ions is required. To sum up, in photochemical applications, especially photodegradation of organic compounds, the presence of Fe(II) ions instead of Fe(III) seems to be more favorable. It is due to the fact that Fe(II) ions are more prone to oxidation processes.
## 5 Measurement Verification of the Effectiveness of the Proposed Method
### The Case of a Monoharmonic Signal  
Although the application of the proposed method does not require prior knowledge of the processed signal, verifying its effectiveness must be based on certain assumptions that allow for determination of the true value of the estimated parameter. In the first experiment, a sinusoidally varying signal described by the following equation was applied to the input of the measurement chain:  
\[\dot{s}(t)=D_{s,\rho}+E_{s,\rho}\sin\bigl{(}\omega_{s,\rho}t+\varphi_{s,\rho} \bigr{)}, \tag{51}\]  
where \(E_{s,\rho}\) is the amplitude, \(D_{s,\rho}\) is the direct current (DC) component, \(\varphi_{s,\rho}\) is the phase, and \(\omega_{s,\rho}\) is the angular frequency of the signal. The source of the \(s(t)\) signal was the RIGOL DG1011 [49] arbitrary waveform generator, and the amplitude setpoints were verified using an additional Agilent 3458A [50] multimeter, assuming that \(E_{s,\rho}=\sqrt{2}E_{RMS}\), where \(E_{RMS}\) is the effective value of the \(s(t)\) signal indicated by the instrument [24].  
The experiment was performed for various frequencies of the \(s(t)\) waveform with amplitude \(E_{s,\rho}=0.479\) V and constant component \(D_{s,\rho}=0.507\) V. The determined relative errors in estimating the variance of the dynamic error signal for this experiment are presented in Table 4, while histograms of the realization values of this quantity for selected experiment parameters are shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c c} \hline \(f_{s,\rho}\), kHz & \(\sigma_{s}^{2}\), mV\({}^{2}\) & \(\sigma_{e}^{2}\), mV\({}^{2}\) & \(\overline{\delta}\),\% \\ \hline
1 & 1.77 & 1.71 & \(-3.67\pm 0.90\) \\
2 & 6.33 & 6.34 & \(0.10\pm 0.07\) \\
3 & 13.0 & 14.2 & \(9.28\pm 0.06\) \\
4 & 21.7 & 25.5 & \(17.38\pm 0.09\) \\
5 & 38.9 & 40.2 & \(3.21\pm 0.04\) \\
6 & 54.1 & 58.7 & \(8.56\pm 0.12\) \\
7 & 68.8 & 80.8 & \(17.48\pm 0.05\) \\
8 & 107 & 107 & \(-0.10\pm 0.13\) \\
9 & 133 & 137 & \(3.28\pm 0.06\) \\
10 & 158 & 172 & \(8.54\pm 0.05\) \\ \hline \end{tabular}
\end{table}
Table 4: Results \({}^{\star}\) of the measurement experiment for the case of the monoharmonic signal \(s(t)\) described in Equation (51) (with expanded uncertainty at a 95% confidence level).
# Optimal Operation of CCHP Smart Distribution Grid with Integration of Renewable Energy
## 2 Mathematical Model of the Components of the Proposed System
### Electric Subsystem
#### 2.1.1 MT-Based CHP Model  
MT-based CHP is considered in this paper to supply electric and heat energy simultaneously. Micro-turbines are fueled by NG. The consumed gas is determined by employing the following equation [33; 34; 35]:  
\[gc_{MT}(t)=U_{MT}(t)\cdot P_{MT}(t)/(L_{NG,MT}\cdot\eta_{MT}) \tag{1}\]where \(P_{MT}(t)\) is the produced electric power of the MT (kW), \(L_{NG,MT}\) is the low heating value (LHV) of the NG (kWh/m\({}^{3}\)), \(\eta_{MT}\) is the efficiency of MT, and \(U_{MT}(t)\) is the on/off case of the MT.  
The CHP generates heat power in addition to electric energy, where the produced heat power is depicted in the following equation:  
\[H_{MT}{}^{t}=U_{MT}{}^{t}\cdot P_{MT}{}^{t}\cdot[1-\eta_{MT}-\delta_{1}]\cdot \delta_{hr}/\eta_{MT} \tag{2}\]  
where \(\delta_{1}\) is the heat loss factor of the MT and \(\delta_{hr}\) is the efficiency of heat recovery. The expense of the NG consumed by the MT is determined as follows:  
\[C_{MT}{}^{t}=gc_{MT}{}^{t}\cdot p_{g} \tag{3}\]  
where \(p_{g}\) is the price of NG (EUR/m\({}^{3}\)).