esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Challenges and Contributions
#### 1.2.2 Modeling Challenges  
There is a significant area for improvement in this stochastic TCP design process as we need to estimate and optimize simultaneously (instead of fully decoupled estimation followed by optimization). In addition, we make several fundamental assumptions (including Poisson arrivals of packets, Gaussanity of underlying processes, etc.) and take system parameters for granted without accounting for their uncertainties.  
With advances in AI and machine learning, the emergence of high-speed wireless networks, the availability of big data, and improved computing capabilities, we can develop interfaces for TCP sources (Delay Adapted Linked Increases Algorithm (DALIA) [11]) to interact with the system and create a new paradigm of MPTCP design using exploration and exploitation. We need to advance our understanding of TCP modeling. Are existing assumptions (Poisson arrivals, Gaussianity [12], etc.) realistic? For example, arrivals in the highly fluctuating IEEE 802.11\(ay\) networks are often more variable (overdispersed [13]) than that captured by the Poisson process due to some external events.Perhaps, today's Internet traffic over IEEE 802.11\(ay\) cannot be captured by a pure Gaussian process [12].) Therefore, we need to rethink the underlying staffing rules [14]. Indeed, there are various features that could not be captured and explained by existing stochastic TCP models [15; 16] for data packet flows in modern wireless networks.  
Another impeding challenge concerns the estimation of the input of queues from observations, so as to detect the changes in arrivals (i.e., changepoint detection [17]). In addition, one would like to have techniques to take into account the uncertainty of the parameters [14]. Data-driven techniques sometimes reveal unforeseen scenarios and phenomena, and it is essential to build robust TCP models with improved queuing-theoretic results that can drive the congestion dynamics in a realistic and desirable fashion. Besides all of these observations, our main focus in this work is on techniques like (i) bandits [18], which can help to explore and exploit in the queuing context and (ii) federated learning [19], which provides a collaborative network interface to give congestion information to the TCP models.
# Parameter Design for Inter-Satellite Laser Link Acquisition Under Weak Light Conditions
## 2 Acquisition System Influencing Factors
### Accumulation of Photoelectrons  
Due to noise such as dark current and readout noise, the acquisition camera still produces output even in the absence of an incident beam. Typically, a threshold is set to determine whether the signal has been acquired. Careful selection of this threshold is crucial: if set too low, noise can be misinterpreted as an optical signal, while setting it too high may prevent successful acquisition. Therefore, we need to determine the number of photoelectrons generated by the camera during the acquisition process.  
The energy accumulated during the acquisition camera's integration time, as the scanning beam sweeps over the receiving SC along a predetermined trajectory \(\vec{s}\), can be considered the energy accumulation of the receiving SC along the trajectory \(s\), as shown in Figure 2.  
Figure 2: Schematic diagram of photoelectron numbers \(S_{sig}\) calculation. Where \(\vec{s}\) is the part of the Archimedes spiral, and \(s\) is the corresponding trajectory of the receiving satellite swept by the SC when the scanning beam is regarded as fixed. If the time interval of \(s\) is relatively small, the integral along the trajectory \(s\) can be approximated as an integral along the trajectory \(s^{\prime}\), as discussed in Section 5.  
According to this and Section 2.1, we can calculate the \(S_{sig}\) by the following:  
\[S_{sig}=\frac{P_{0}Q_{e}}{h\
u}\int_{t_{0}}^{t_{0}+\Delta t}e^{-\frac{2 gt|t|^{2}} {\
u_{0}^{2}}}dt, \tag{5}\]  
where \(S_{sig}\) is the total number of photoelectrons in the spot location window of N pixels during the integration time \(t_{int}\), \(h\
u\) represents the photon energy, \(Q_{e}\) is the quantum efficiency of the detector, \(t_{0}\) is the start time for the integration, and \(\Delta t\) is any time interval within the integration time. Since the scanning trajectory we have chosen is an Archimedes spiral, determining the trajectory \(s\) theoretically is challenging due to the unknown positions of the receiving SC and \(t_{0}\). However, we can calculate the threshold for the acquisition by determining the minimum number of photoelectrons required to meet the laser spot center positioning accuracy.
## 5 Discussion
### Surface Subspace and Urbanization Development  
In recent years, with the rapid development of thermal infrared remote sensing, many countries have launched notilucent remote sensing satellites.  
Nighttime light remote sensing refers to the use of remote sensing technique to capture the light distribution on the earth at night, which can effectively reflect the spatial distribution of human activities, so it is often used for remote sensing inversion of urban development [61; 62; 63].  
In this paper, VIIRS notilucent remote sensing satellite sensor was used to obtain the notilucent remote sensing data of the main urban area of Shanghai, and the difference processing of the nighttime light imageries was carried out to obtain the urbanization development changes in Shanghai from April 2018 to April 2020. Due to the poor imaging effect of the monthly composite data from January 2018 to March 2018 published by VIIRS, this paper selected the monthly composite products from April 2018 and April 2020 published by VIIRS, as shown in Figures 16 and 17. After the difference processing, the change in nighttime light imageries in Shanghai from April 2018 to April 2020 is obtained in this paper (Figure 18). The overall trend of urbanization in Shanghai is developing rapidly. Among them, it is located in the southeast of Shanghai Jinshan District and the southern cross of light brightness rising big scope, which reflect regional urbanization and the development of infrastructure construction. At the same time, compared with Figure 4, the subsidence of this area has a high correlation with the change in light brightness. This finding also proves that the process of urbanization and infrastructure construction will further increase the pressure on the surface, thereby resulting in uneven subsidence. The results show that urbanization and infrastructure construction are positively correlated with surface subsidence to a certain extent.  
Figure 15: Water level change of water level monitoring point D in 2019.  
Figure 16: Night lighting data of main urban area of April 2018.  
Figure 17: Night lighting data of main urban area of Shanghai in Shanghai in April 2020.
## 4 Parametric Analysis
### Soil Elastic Modulus  
The effects of the soil elastic modulus on the building response are shown in Figure 11. The increase in the soil elastic modulus can obviously decrease the building settlement (Figure 11a), which is because the stiffer soil can provide larger resistance to resist the building settlement due to the stress relief. The magnitude of the decrease in the building settlement decreases as the soil elastic modulus increases. With the increase in the soil elastic modulus, the rotation angles gradually decrease in the range \(y\leq 10.0\) m and increase in the range \(y>10.0\) m (Figure 11b). The maximum and minimum rotation angles occur at \(y=20.0\) m and \(y=0\), respectively. The bending moments greatly increase and become less sensitive to the soil elastic modulus as the soil elastic modulus increases (Figure 11c). The maximum positive bending moment occurs at the middle of the building, regardless of the variation in the soil elastic modulus. When the soil elastic modulus increases from 20.0 to 60.0 MPa, the maximum positive shear force occurring at \(y=4.0\) m and the maximum negative shear force increase accordingly (Figure 11d). Additionally, the position of the maximum negative shear force moves from \(y=16.0\) m to \(y=17.0\) m.  
Figure 10: Building response with different distances from tunnel face: (**a**) settlement; (**b**) rotation angle; (**c**) bending moment; (**d**) shear force.
# Stock Market Prediction Using Microblogging Sentiment Analysis and Machine Learning
## 3 Research Design
### Data Preprocessing
#### 3.2.2 Outlier Removal  
After collecting the data, we standardized the sentiment score around the mean to be equal to zero with a standard deviation of one. Importing the class 'StandardScaler' and its methods from the'sklearn.preprocessing' module [38], we enforced data consistency so that each data point has the same range and variability. Then, outliers in the sentiment dataset were deleted. Outliers endanger the model's validity, since they make up a significant portion of the primary datasets, as seen in Table 4.  
Therefore, to reduce bias, significant positive or negative rates were excluded. We utilized the 10% for small rates as flooring and the 90% for large rates as topping thresholds, respectively.
# Greenhouse Temperature Prediction Based on Time-Series Features and LightGBM
## 5 Results and Analysis  
### Evaluation Indicator  
In quantitatively evaluating differences in the fitting degree between models and between original features and time-series features, the mean square error (MSE), mean absolute error (MAE), and R-square coefficient of determination (\(R^{2}\)) were used as evaluation indicators to quantitatively distinguish and compare the prediction results of each model. The formulas for calculating the evaluation indicator are:  
\[\text{MSE}(y,\dot{y})=\frac{1}{n}{\sum_{i=0}^{n-1}}(y_{i}-\dot{y_{i}})^{2}, \tag{15}\]  
\[\text{MAE}(y,\dot{y})=\frac{1}{n}{\sum_{i=0}^{n-1}}\left|y_{i}-\dot{y_{i}}\right|, \tag{16}\]  
\[R^{2}(y,\dot{y})=1-\frac{{\sum_{i=0}^{n-1}}(y_{i}-\dot{y_{i}})^{2}}{{\sum_{i= 0}^{n-1}}(y_{i}-y^{-})^{2}}, \tag{17}\]  
where \(y_{i}\) is the actual value and \(\dot{y_{i}}\) is the predicted value of the \(i\)-th group of performance evaluation data, \(y^{-}\) is the average value of data, and n is the total number of samples. MSE  
Figure 4: Importance of each feature evaluated using the tree model.  
and MAE, respectively, denote the MSE and MAE of the predicted value and actual value. A smaller index value indicates a better prediction. The \(R^{2}\) coefficient of determination indicates the degree of fitting between the predicted value and the actual value.
# A Robust and Energy-Efficient Weighted Clustering Algorithm on Mobile Ad Hoc Sensor Networks +
## 3 Description of RE2 WCA Algorithm
### Weight Model and Hybrid Clustering Algorithm
Therefore, if initial \(p_{thr}\) is greater than \(ER\), \(p_{thr}\) will be chosen as the iteration threshold. Considering energy efficiency, if the residual energy of cluster head is enough, the iteration times of cluster head can be reduced. On the contrary, if the energy of cluster head is not adequate, the network will need more iteration times to update the perfect cluster head. In conclusion, according to Algorithm 2, the process of cluster head election is demonstrated as follows:  
```
Function: \(weight(i)=C_{prob}\times\left[a\times\frac{E_{i}^{residual}}{E_{i}^{max}}+b \times CHSD_{map}(i)\right]\); \(CH_{prob}\leftarrow\max(weight(i),p_{min})\); \(p_{min}\leftarrow\max\{ER,\,pthr\}\);  Input: \(a\), \(b\leftarrow\)weight(i) factors satisfy \(a>b\) and \(a+b=1\); \(C_{prob}\leftarrow\) assuming value; \(d\leftarrow\) distance between sending end and receiving end;
1. Begin
2. The sending end radio energy consumption is \(E_{t}\) if \(d<d_{0}\), the free space model is used, otherwise, multi-path attenuation model is used
3. The receiving end radio energy consumption is \(E_{r}(t)=IE_{dec}\)
4. \(E_{residual}\leftarrow\) the difference between initial energy and the summation of sending and receiving energy
5. \(\hat{\theta}_{i},\hat{\theta}_{ch},\hat{S}_{i},\hat{S}_{ch}\leftarrow\) group mobility process. \(RD(i,ch,t)=\cos\left(\hat{\theta}_{i}(t)-\hat{\theta}_{ch}(t)\right)\); \(SR(i,ch,t)=\frac{\min\left(\hat{S}_{r}(t),\hat{S}_{ch}(t)\right)}{\max\left( \hat{S}_{r}(t),\hat{S}_{ch}(t)\right)}\)
6. \(CHSD(i,ch)\gets RD(i,ch)\times SD(i,ch)\) at one moment \(t\); then a linear mapping from \(CHSD\) to \(CHSD_{map}\). 7. Start \(CH\) selection protocol to implement \(CH\) election. 8. End. ```  
**Algorithm 2**\(CH\) election algorithm (each round for all nodes within the network)
## Appendix E Evidence for a High Energy Process Contributing to the Heat Flow at High Temperatures  
Figure 14 shows the (not baseline corrected) DSC heat flow data for dynamic experiments with temperature ramps of 1 K/min. The two measurements were approximately half a year apart.  
The black curve in Figure 14 was measured with material for which resin from a certain barrel was used. All dynamic experiments presented in Section 7 were conducted within a couple of days with this resin. For the red curve in Figure 14 resin from the same barrel was used but half a year later. Around ca. 415 K a second peak occurs in the red curve. This peak may manifest itself as a slightly higher \"shoulder\" in the black curve at approximately the same temperature. Albeit one has to know where and what to look for.  
The amount of heat released by this second peak explains the discrepancy in the mean heat flow values between the isothermal and dynamic experiments.  
The second peak does not manifest itself so clearly in the dynamic measurements with the \"younger resin\" presented in Section 7. Nonetheless, we assume that the (unknown) process(es) which are responsible for it are taking place during the isothermal period after the temperature ramp. Hence, these processes likely lead to a (slightly) larger heat flow signal and thus the activation energy is different from the activation energy calculated with the isothermal data.  
After the discovery of this second peak several control experiments were performed and it was confirmed that it does not occur at the temperatures at which the isothermal experiments were conducted. Hence, we assume that this process needs a higher activation energy than the curing process itself. Thus the activation energy determined by the dynamic DSC data diverges for higher conversion, since higher conversion occurs for high temperatures in dynamic experiments.  
Control experiments with a previously unopened barrel of resin were conducted and the same second peak was observed. Said barrel was bought at the same time as the previous barrel, so the resin is likely from the same batch, but was not opened before the control experiments. Thus, the resin was not exposed to air. Hence, we assume that the process leading to the second peak is inherent to the material (and thus always present) and becomes more pronounced for older resins. We do not assume that it is due to chemical reactions taking place in the barrel between the resin and the air, since the resin for said control experiments were not exposed to the air for a prolonged time before the experiments.  
We are aware that all these assumptions are unsatisfactory but the producer of the resin does not provide more information. From a practical point of view the second peak does explain the observed differences in the total heat and the diverging activation energies determined from dynamic experiments. Thus we think it is justified to use the mean of the total heat determined from isothermal measurements for the simulations and a constant activation energy for \(\alpha>50\%\).
# Fast Sound Source Localization Based on SRP-PHAT Using Density Peaks Clustering
## 6 Real Environment Test
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number** & \(\theta\) & \(\varphi\) & **Number** & \(\theta\) & \(\varphi\) \\ \hline \(q_{1}\) & 319 & 52 & \(q_{10}\) & 126 & 54 \\ \(q_{2}\) & 301 & 50 & \(q_{11}\) & 111 & 63 \\ \(q_{3}\) & 281 & 41 & \(q_{12}\) & 93 & 66 \\ \(q_{4}\) & 256 & 44 & \(q_{13}\) & 73 & 64 \\ \(q_{5}\) & 235 & 41 & \(q_{14}\) & 53 & 62 \\ \(q_{6}\) & 209 & 47 & \(q_{15}\) & 32 & 67 \\ \(q_{7}\) & 191 & 48 & \(q_{16}\) & 17 & 59 \\ \(q_{8}\) & 168 & 50 & \(q_{17}\) & 357 & 56 \\ \(q_{9}\) & 147 & 54 & \(q_{18}\) & 339 & 55 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: ODB of the meeting room. Figure 10: Clustering analysis results (18 positions). In Figure 11, the success rate of the localization for ODB-SRP-PHAT is generally high. Except for seats 3, 4, and 6, the \(V\) values of the other seats are all greater than 90%. In particular, from seat 12 to seat 18 near the open door, the \(V\) values approach 100%. The lower \(V\) values for seat 3, 4, and 6 were due to a close wall having a smooth surface behind these seats, which resulted in a stronger reverberation. When the tolerance was set as 20\({}^{\circ}\), the success rates from SRP-PHAT were close to those from ODB-SRP-PHAT. When the tolerance was 10\({}^{\circ}\), however, the \(V\) values from ODB-SRP-PHAT were much larger than those from SRP-PHAT. ODB-SRP-PHAT demonstrated clear superiority in the sound source localization. Regarding the computational load, when using SRP-PHAT for analysis, the search range of the azimuth angle was 1\({}^{\circ}\)-360\({}^{\circ}\), the elevation angle was 30\({}^{\circ}\)-80\({}^{\circ}\), and the search step was 1\({}^{\circ}\), Therefore, there were 18,360 (360 \(\times\) 51) grid points at which the SRP value needed to be calculated. ODB-SRP-PHAT only needed to compute the SRP values at the 18 points in the ODB. The online computational load was less than 0.1% of that by SRP-PHAT. In general, ODB-SRP-PHAT was validated by the tests in the real environment. ODB-SRP-PHAT could localize the sound source accurately with the least computational load. In addition, it had a good ability to resist noise and reverberation. ODB-SRP-PHAT is applicable for online sound source localization.
# Fractional Order Controller Design for Wind Turbines
## 2 Materials and Methods
### Fractional Order PID (FOPID) Controller
Therefore, the following transformation had to be derived for the fractional order \(\alpha\):  
\[s^{\alpha}=(j\omega)^{\alpha}=e^{j\frac{\pi\alpha}{2}}=\omega^{\alpha}\text{ cos}\left(\frac{\pi\alpha}{2}\right)+j\omega^{\alpha}\text{sin}\left(\frac{\pi \alpha}{2}\right) \tag{13}\]  
The gain crossover frequency and the phase margin were calculated using Equations (14) and (15), respectively:  
\[\mid C(j\omega_{gc})G(\omega_{gc})\mid\text{ dB}=0\text{ dB} \tag{14}\]  
\[arg(C(j\omega_{gc})G(j\omega_{gc}))=-\pi+\phi_{m} \tag{15}\]  
where with \(C\) is the controller and \(G\) represents the process model. The complementary sensitivity function (Equation (16)) and the sensitivity function (Equation (17)) were also used as constraints for the system:  
\[\mid T(j\omega)=\frac{C(j\omega)G(j\omega)}{1+C(j\omega)G(j\omega)}\mid_{ \text{dB}}\ \leq\ A_{\text{dB}}; \tag{16}\]  
where \(A\) is the designed noise attenuation for frequencies \(\omega\geq\omega_{gc}\) rad/s, and:  
\[\mid S(j\omega)=\frac{1}{1+C(j\omega)G(j\omega)}\mid_{\text{dB}}\ \leq\ B_{\text{dB}}; \tag{17}\]  
where \(B\) is the desired value for frequencies \(\omega\ \leq\omega_{gc}\) rad/s. Equation (18) represents the iso-damping property, which was used to ensure that the process was more robust in the presence of gain changes and that the overshoot was close to a constant value within the gain range [26]:  
\[\frac{d(arg(C(j\omega)\cdot G(j\omega)))}{d\omega}\mid\omega=\omega_{gc} \tag{18}\]  
As for the main function that had to be minimized, the IAE function that was imposed produced the best results compared to other performance indices, such as ITAE, ITSE, ISE:  
Figure 3:0  
The controllers were evaluated using the steady-state error, overshoot, settling time, IAE and robustness to noise and disturbances. Additionally, it should be mentioned that all three algorithms were labeled in the MATLAB script with the following function form:  
Figure 3:1  
where fun is the main function that the GA, MOGA and PSO algorithms had to minimize, x0 is the initial value of the FOPID parameters that had to be tuned, lb is the lower bound and ub is the upper bound. For the options section, the optimization algorithm that was used had to be specified (i.e., \"_ga_\", \"_gamultiobj_\" or \"_particleswarm_\") and for the constraint function in MATLAB, we had to create a separate function in which all of the equalities and inequalities were recorded in \(C_{\text{eq}}\) (for equalities functions) and \(C\) (for inequalities constraints) vectors. Each optimization algorithm was labeled separately. Furthermore, the following subsections of the article present brief descriptions of the optimization algorithms and the options and main functions that were used in each case.
# Benchmarking Change Detector Algorithms from Different Concept Drift Perspectives
## 4 Material and Methods
### Evaluation Metrics  
The drift detectors' performances were assessed according to three main criteria: detection accuracy, detection time, and delay to detect drifts. In this section, we provide further details on how the metrics for these criteria were calculated.
## 2 Materials and Methods: Ten-Koh Mission Description
### Challenges Faced during the Project
#### 2.7.1 Available Generated Power  
The minimum available power in the worst-case scenario was computed to be 11 W, which was found sufficient for the maximum expected power consumption of 9.5 W. The EPS and the batteries were sized to be able to support such power consumption in eclipse for up to 30 min, which was enough to gather CPD data around the poles, regions of specific interest due to abundance of charged particles. The EPS was not sized to be able to support maximum power consumption indefinitely due to the limited surface area that could be covered with solar cells. Using a deployable solar array could remedy this area constraint but would require development of complex on-board software and mechanisms, and was thus deemed unfeasible. Attitude control would also be needed if a deployable array was used, which is described in a separate section.