esa-sceva/satcom-chunk-collection huggingface dataset:
# Parameter Estimation for Some Probability Distributions Used in Hydrology
## 3 Methods
### Methods of Parameter Estimation
#### 3.2.7 Generalized Pareto Distribution (P3)  
For estimation with MOM, parameter \(\alpha\) has the following approximate polynomial or rational forms depending on \(\mathrm{C}_{\mathrm{s}}\):  
\[\alpha=999.99815689\cdot 10^{-3}-1.154659839\cdot\mathrm{C}_{\mathrm{s}}+555.214061169\cdot 10^{-3}\cdot\mathrm{C}_{\mathrm{s}}^{2}-\\ 137.553895649\cdot 10^{-3}\cdot\mathrm{C}_{\mathrm{s}}^{4}-2.365065448\cdot 10^{-3} \cdot\mathrm{C}_{\mathrm{s}}^{4}+13.308919064\cdot 10^{-3}\cdot\mathrm{C}_{ \mathrm{s}}^{5}-\\ 3.931108819\cdot 10^{-3}\cdot\mathrm{C}_{\mathrm{s}}^{6}+409.717304137\cdot 10^{-6} \cdot\mathrm{C}_{\mathrm{s}}^{5}\end{array} \tag{88}\]  
\[\gamma=\mu-\beta\cdot\left(\psi\left(\frac{1}{\alpha}+1\right)+\ln(\alpha)+\gamma_{ \mathrm{e}}\right) \tag{107}\]  
where \(\gamma_{\mathrm{e}}\) is the Euler-Mascheroni constant and \(\psi(..)\) represents the digamma function, which has the following approximate form [11]:  
\[\begin{array}{l}\psi(\alpha)=\frac{\mathrm{d}}{\mathrm{d}\alpha}(\ln(\Gamma( \alpha)))=\mathrm{Psi}(\alpha)\\ \psi(\alpha)\approx\ln(\alpha+2)-\frac{1}{\alpha}-\frac{1}{\alpha+1}-\frac{1} {2\cdot(\alpha+2)}-\frac{1}{12\cdot(\alpha+2)^{2}}+\frac{1}{120\cdot(\alpha+2) ^{4}}\end{array} \tag{108}\]  
For estimation with the L-moments, parameter \(\alpha\) has the following approximate forms depending on \(\tau_{3}\):  
\[\begin{array}{l}\mathrm{For}\ 0.17\leq|\tau_{3}|\leq\frac{1}{3}\text{:}\\ \\ \alpha=-971.10886182\cdot 10^{-3}+6.3606944\cdot|\tau_{3}|-6.35405524\cdot\tau_{3}^{ 2}+15.03572719\cdot|\tau_{3}|^{3}\end{array} \tag{109}\]  
for \(\frac{1}{3}<|\tau_{3}|<1\):  
\[\alpha=\frac{-661.76303922\cdot 10^{-3}+4.30832382\cdot|\tau_{3}|-1.09504916 \cdot\tau_{3}^{2}+373.12384033\cdot 10^{-3}.|\tau_{3}|^{3}}{1-999.98952576\cdot 10^{-3}.|\tau_{3}|} \tag{110}\]  
\[\beta=\frac{\mathrm{L}_{2}}{\psi\left(\frac{2}{\alpha}+1\right)-\psi\left( \frac{1}{\alpha}+1\right)} \tag{111}\]  
\[\gamma=\mathrm{L}_{1}-\beta\cdot\left[\psi\left(\frac{1}{\alpha}+1\right)+ \gamma_{\mathrm{e}}+\ln(\alpha)\right] \tag{112}\]
## 4 Numerical Results
### Transmission Reliability
Figure 6b shows the simulation results of the change of bit error rate with a signal-to-noise ratio under two conditions for optimizing the solution order of signal spatial direction and random solution. According to the performance comparison in the Figure, the bit error rate is lower, and the system performance is significantly improved when the signal space order is determined. Therefore, the simulation results can verify that the maximum selection gain can be obtained by first solving the spatial alignment direction formed by the interactive signal corresponding to the worst transmission channel. The spatial alignment direction formed by the interactive signals corresponding to the channel with the best transmission quality is finally solved to achieve the balance of the system performance bottleneck. Figure 6c shows the MIMO-Y channels of 10 users with antenna configurations of \(45\times 45\). The BER of the system varies with the SNR when different channel transmission quality criteria are used to determine the signal space alignment direction. It can be seen from the Figure that in the multiuser application scenario, the normal distribution of singular values of the channel matrix is comprehensively considered. At the same time, the scheme based on the variance of the singular value of the channel matrix to measure the channel quality is better than the scheme based on the mean value. Figure 6d shows the MIMO-Y channels of three users with an antenna configuration. Through four different communication schemes, the relationship between the system error rate and the change of SNR is compared. The four schemes are traditional signal space  
Figure 7 compares the BER performance of different schemes after suppressing co-channel interference. MIMO beamforming takes up the same time-frequency resources and uses spatial resources to divide spatially separable transmission paths. Its transmission quality is significantly better than that of multiple parallel data streams constructed by a MIMO multiuser. Transformed Subspace [32] allows messages to be exchanged by applying improved signal space alignment for network coding and interference cancelation when antennas configuration extends to \(N>2M\). The proposed scheme develops the potential of signal space and constructs mutually orthogonal signal space alignment directions. According to channel quality selection and power allocation, the minimum European distance of constellation points is increased. This can improve the BER performance of the system. Therefore, the BER performance of the proposed scheme is the best.
# A New Orbiting Deployable System for Small Satellite Observations for Ecology and Earth Observation
## 3 Study Cases  
In this section, we present an overview of possible applications that would benefit from remote sensing data, in particular in the new perspective provided by small satellites. The applications discussed in this paper cover highly dynamic processes in the environments ranging from mid-latitude to polar seas and atmospheres. See Table 2 and Table S1 in the Supplementary Material for details about the small and large satellites, respectively, mentioned in this section.
## Appendix A Pertinence of the Representative Variable Detection Model  
We illustrate, in this section, the notion of coherence defined in Section 2.2. We simulated an adjacency matrix that mimics the absolute values of the Pearson correlations between 15 variables. This symmetric matrix is shown in Figure 1(top-left) and represents graph \(G_{1}\) following the method of Section 2.1. Each of its values is then equal to a similarity level encoded in \(w_{i,j}\) between two variables \(X^{i}\) and \(X^{j}\), where \(i\) and \(j\) are in \(\{0,\cdots,14\}\). We can clearly remark that it contains two blocks of related variables \(S^{1}_{Ref}=\{X^{0},\cdots,X^{6}\}\) and \(S^{2}_{Ref}=\{X^{8},\cdots,X^{14}\}\) and an independent variable \(X^{7}\). In addition, variables \(X^{3}\) and \(X^{11}\) are slightly more related to other variables in \(S^{1}_{Ref}\) and \(S^{2}_{Ref}\),respectively. They can then be considered as the most pertinent representative variables in these blocks. We also added to \(G_{1}\) undesirable relations having an intermediate level between the variables \(\{X^{1},\cdots,X^{5}\}\) and \(\{X^{9},\cdots,X^{13}\}\) and saved the result in graph \(G_{2}\), as shown in Figure 1(top-right). More quantitatively, the reference blocks \(S^{1}_{Ref}\) and \(S^{2}_{Ref}\) have inner similarities sampled following the normal law \(\mathcal{N}(0.75,0.1)\); the background ones are sampled following \(\mathcal{N}(0.,0.1)\), and in Figure 1, the undesirable relations are sampled following \(\mathcal{N}(0.37,0.1)\). In each reference block, the relation between the simulated reference variables and other variables are finally sampled following \(\mathcal{N}(0.9,0.1)\). The norms of these similarities are then considered and the similarities higher than one are set to one.  
We will measure hereafter the coherence of different variable subsamples to illustrate this notion and make clear its interest in the CORE-clustering context. As the CORE-clustering algorithms proposed in Section 3 use maximal spanning trees, we will additionally discuss the corresponding coherences obtained on the maximal spanning trees of \(G_{1}\) and \(G_{2}\), which are shown in Figure 1(bottom). The estimated representative variables will finally be given in all tests to make sure that their estimation is robust.
# On the Adaptive Penalty Parameter Selection in ADMM
## 3 Numerical Experiments  
In this section we present the results of the numerical experiments performed to assess the performances of Algorithm 1 equipped with different choices for the update of \(\tau^{k}\) in STEP 4. We compare the alternating strategy used in [7] with BB1-like rules \(\alpha_{k}^{BB1}\) (16)-(18), BB2-like rules \(\alpha_{k}^{BB2}\) (17)-(19), and a different alternating strategy, based on a modified version of the ABB\({}_{\min}\) rule [18], introduced in [19]. The five algorithms compared in this section are the following:  
* \"Vanilla ADMM\", in which \(\tau^{k}\) is fixed to \(\tau_{0}\) throughout all iterations;
* \"Adaptive ADMM\"[7], in which \(\alpha_{k}\) and \(\beta_{k}\) are set to \[\alpha_{k}=\left\{\begin{array}{ll}\alpha_{k}^{BB2},&\text{if $2\alpha_{k}^{BB2}> \alpha_{k}^{BB1}$},\\ \alpha_{k}^{BB1}-\frac{\alpha_{k}^{BB2}}{2},&\text{otherwise},\end{array} \right.\beta_{k}=\left\{\begin{array}{ll}\beta_{k}^{BB2},&\text{if $2\beta_{k}^{BB2}> \beta_{k}^{BB1}$},\\ \beta_{k}^{BB1}-\frac{\beta_{k}^{BB2}}{2},&\text{otherwise};\end{array}\right.\]
* \"Adaptive ADMM-BB1\", in which \(\alpha_{k}\) and \(\beta_{k}\) are set to \(\alpha_{k}^{BB1}\) and \(\beta_{k}^{BB1}\), respectively;
* \"Adaptive ADMM-BB2\", in which \(\alpha_{k}\) and \(\beta_{k}\) are set to \(\alpha_{k}^{BB2}\) and \(\beta_{k}^{BB2}\), respectively;* \"Adaptive ADMM-\(\text{ABB}_{\text{min}}\)\", in which \(\alpha_{k}\) and \(\beta_{k}\) are set to \[\alpha_{k}^{ABB_{\text{min}}}=\begin{cases}\min\Bigl{\{}\alpha_{j}^{BB2}\,:\,j= \max\{1,k-m_{\alpha}\},\ldots,k\Bigr{\}},&\text{if }\alpha_{k}^{BB2}<\delta_{k}\alpha_{k}^{BB1},\\ \alpha_{k}^{BB1},&\text{otherwise},\end{cases}\] \[\beta_{k}^{ABB_{\text{min}}}=\begin{cases}\min\Bigl{\{}\beta_{j}^{ BB2}\,:\,j=\max\{1,k-m_{\alpha}\},\ldots,k\Bigr{\}},&\text{if }\beta_{k}^{BB2}<\delta_{k}\beta_{k}^{BB1},\\ \beta_{k}^{BB1},&\text{otherwise},\end{cases}\] where \(m_{\alpha}=2\), \(\delta_{0}=0.5\), and \(\delta_{k}\) is updated as follows: \[\delta_{k+1}=\begin{cases}\begin{array}{c}\delta_{k}/1.2,\quad\text{if } \alpha_{k}^{BB2}<\delta_{k}\alpha_{k}^{BB1}(\text{or }\beta_{k}^{BB2}<\delta_{k}\beta_{k}^{BB1}),\\ \delta_{k}\cdot 1.2,&\text{otherwise}.\end{array}\end{cases}\] For all the algorithms, following [7], we set \(\epsilon_{\text{cor}}=0.2\) and \(\bar{n}=2\). The methods stop when the relative residual is below a prefixed tolerance \(tol\;>0\) within a maximum number of iterations, where the relative residual is defined by \[\max\Biggl{\{}\frac{\|r_{p}^{k}\|_{2}}{\max\{\|Eu^{k}\|_{2},\|Fv^{k}\|_{2},\|d \|_{2}\}},\frac{\|r_{d}^{k}\|_{2}}{\|E^{\top}\xi^{k}\|_{2}}\Biggr{\}},\] (21) with \(r_{p}^{k}=d-Eu^{k}-Fv^{k}\) and \(r_{d}^{k}=\tau^{k}E^{T}F(v^{k}-v^{k-1})\). All experiments were performed using MATLAB. Recently, the PADPD algorithm [20] has been proposed as an algorithm analogous to ADMM, but it has fixed stepsize and is out of the scope of this paper to compare.
# An RTK UAV-Based Method for Radial Velocity Validation of Weather Radar
## 4 Results  
### Design and Validation of UAV Routes  
The experiment obtained volume scan data within a 3 km range under clear sky conditions, with the aim of finding a flight area with a relatively clean background and minimal interference from ground clutter. To ensure the safety of the UAV operation, the experiment avoided Mount Lianhua in the north of the X-band radar calibration site. Instead, 180\({}^{\circ}\) was used as the experimental azimuth. After selecting the experimental orientation, the information from each elevation angle layer of the volume scan data was analyzed. The red box in Figure 7 shows that there was a target with an intensity of more than 10 dB at an elevation angle of 6.0\({}^{\circ}\) in the selected orientation, whereas at 9.9\({}^{\circ}\) the intensity of the same target was less than 5 dB. In view of the maximum flight altitude of the UAV being 500 m, an actual radar observation elevation angle of 8.5\({}^{\circ}\) was ultimately selected for the experiment.  
The flight route of the UAV was required to meet the far-field condition of the X-band radar at 360 m and maintain a constant velocity over an elevation angle of 8.5\({}^{\circ}\), using the antenna feeds as a reference. Therefore, the start point for the route was chosen at a slant range of 650 m from the radar. The UAV flew at a constant velocity over 600 m, covering eight range bins, and finished at a slant range of 1250 m from the radar. With the DEM data of the experimental site taken into consideration (Figure 8), the flight route effectively avoided the surrounding mountainous terrain, ensuring the safety of the round-trip flight routes.  
Figure 8: Radial velocity validation of the UAV routes.  
Figure 7: X-band radar volume scan data. (**a**) Elevation angle: 6.0\({}^{\circ}\); (**b**) Elevation angle: 9.9\({}^{\circ}\).  
To verify the relative position between the UAV route and the radar, the experiment compared the actual flight elevation of the UAV with the elevation of the radar main beam center. Based on the latitude, longitude, and radar elevation angles collected by the RTK system on the UAV, and with reference to the geometric position relationships shown in Figure 2, the experiment calculated the elevation of the radar main beam center and the beam widths for each point. We define the deviation between the UAV's actual flight elevation and the elevation of the radar main beam center as the elevation deviation. Combining Table 5 and Figure 9 for different velocities, the maximum ratio of the elevation deviation to the beam width occurs near the proximal end of the route, which is closer to the radar system. The maximum elevation deviation is 1.
## 1 Introduction
The commercial enzyme can suffer modifications during purification and dehydration, especially in relation to the tridimensional structure of the protein part, and can partially lose specificity for specific analysts. It can be remarked that in the raw extract, the enzyme is surrounded by the cofactors and other compounds from the biological cells, and, therefore, the biocatalytic properties are well preserved and useful in the electrochemical detection. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sensitive Material** & **Detection Technique** & **Analyte** & **LOD (\#M)** & **Reference** \\ \hline HRP/thiol-modified gold & \begin{tabular}{c} Amprometry \\ \end{tabular} & (+)-Catechin & 2.2 & [34] \\ Au/MPA/HRP & \begin{tabular}{c} Amprometry \\ \end{tabular} & \begin{tabular}{c} Guaiacol \\ \end{tabular} & 0.8 & [35] \\ SAP/HRP/Au-GN & \begin{tabular}{c} Linear Scanning \\ \end{tabular} & \begin{tabular}{c} Butylated hydroxyapatite \\ \end{tabular} & 0.255 & [36] \\  & \begin{tabular}{c} Voltammetry \\ \end{tabular} & \begin{tabular}{c} Propyl gallate \\ \end{tabular} & 0.113 & [37] \\ Poly(Gly)/SiSG/MWCNTs/HRP & \begin{tabular}{c} Differential pulse \\ voltammetry \\ \end{tabular} & \begin{tabular}{c} Dopamine \\ \end{tabular} & 0.6 & [38] \\ Con A-HRP & \begin{tabular}{c} Amprometry \\ \end{tabular} & \begin{tabular}{c} Patechol \\ \end{tabular} & 0.6 & [39] \\ HRP/DNA & \begin{tabular}{c} Amprometry \\ \end{tabular} & \begin{tabular}{c} On-Cresol \\ Chlorogenic acid \\ \end{tabular} & 0.4 & [40] \\ Poly(GMA-co-MTM)/PPy/CNT/HRP & \
\begin{tabular}{c} Amprometry \\ \end{tabular} &
\begin{tabular}{c} Hydroquinone \\ \end{tabular} & 0.336 & [41] \\ \hline HRP\(-\)horseradish peroxidase; Au/MPA/HRP\(-\)sol/mercaptoumdecanoic acid/horseradish peroxidase; SAP/HRP/Au-GN--Spiny Au-Pt nanotubes/horseradish peroxidase/Au-graphene; HRP/DNA-horseradish peroxidase/DNA; Con A/HRP\(-\)concanavalain A/horseradish peroxidase Poly(Gly)/SiSG/MWCNTs/HRP\(-\)DNA\(-\)poly (Glycine)/silica sol-gel/multiwalled carbon nanotubes/horseradish peroxidase; Poly(GMA-co-MTM)/PPy/CNT/HRP\(-\)poly(glycine methacrylate-co-3-thienylly methacrylate)-polypyrrole-carbon nanotube-horseradish peroxidase. & 0.247 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Peroxidase-based biosensors used to detect phenolic compounds. The main novelty of this study is the use of horseradish root extract containing peroxidase for the construction of new biosensors using screen-printed electrodes as support electrodes and Prussian blue as an electron exchange mediator for the detection of caffeic acid. Based on our knowledge, it is the first time a biosensor based on Prussian blue and peroxidase has been developed for the detection of caffeic acid. Spectrometric methods will be used on horseradish root extract to identify and characterize enzymatic activity. The biosensors will be developed, characterized, and successfully applied for the qualitative and quantitative determination of caffeic acid in standard solutions and real samples.
# An Empirical Approach for Extreme Behavior Identification through Tweets Using Machine Learning
## 3 Materials and Methods
### Performance Evaluation  
Some of the metrics that are used to evaluate the effectiveness of the classifiers are discussed in the subsequent sub-sections.  
\(\bullet\) Accuracy  
Accuracy is the proportion of the correct prediction made by the model. In simple term, it is the percentage of all the input samples in the dataset that are correctly classified such as, if the model correctly predicts 45 data samples out of 50, the accuracy of the model would by 90%.  
\[\text{Accuracy}=\ \frac{\text{correctly predict sample}}{\text{total samples}}\times 100 \tag{4}\]  
\(\bullet\) Precision, Recall, and F-score  
Precision can be defined as the ratio of true positive predicted and total positive in the data set and it measures the performances with respect to correct prediction. The greater precision means the less miss predicted hits. The recall is the ability of the classifier to predict as many true positive out of the total expected. The performance will be better as precisions and recall have greater values. F-score is the harmonic mean of recall and precision. F-score describes that how precise is the model by taking a mean of precision and recall.  
\[\text{Precision}=\frac{\text{True Positive}}{\text{True Positive}+\text{ False Positive}} \tag{5}\]  
\[\text{Recall}=\frac{\text{True Positive}}{\text{True Positive}+\text{ False Negative}} \tag{6}\]  
\[\text{F-score}=2\times\ \frac{\text{Precision }\times\text{Recall}}{\text{Precision }+\text{Recall}} \tag{7}\]
# Data Imputation Based on Retrieval-Augmented Generation
## 4 Experimental Verification
### Evaluation of the Overall Experimental Effect of Data Imputation
#### 4.1.1 Dataset Preparation  
To make our experiments more compelling, we collected and preprocess datasets from various domains, as described below.  
Figure 5: Code process.  
**Dataset Collection**: Our goal is to showcase datasets with specific features: (1) they should originate from real-world scenarios, and (2) they should cover a variety of data domains. Guided by these criteria and existing work on missing value imputation, we collected the three datasets from real-world data sources:  
* WikiTuples [99]: This dataset is composed of 207,912 tables from various fields within the data lake, such as sports, politics, and arts. It contains 10,003 tuples, with missing values present in 807 tables. These missing values span multiple attributes, including Party, Director, Team, Album, etc. On average, each tuple with missing values corresponds to 4.38 target tuples, reflecting the complexity of locating target tuples among 2,674,164 tuples in the data lake. Although WikiTuples and the retriever's training data come from the same source, the latter did not undergo the same rigorous annotation process. Furthermore, only 41,000 tables were used during the pre-training phase, which is significantly smaller than the size of the data lake.
* Education [100]: This dataset includes information about elementary and high schools in Chicago, containing 654 tuples, with missing values in the Address, Zip Code, and Phone Number columns. It comprises 11,132 tuples, with each tuple missing values corresponding to four target tuples.
* Cricket Players [101]: The Cricket Players dataset also originates from RetClean and focuses on the sports domain. It contains 213 incomplete tuples, with the data lake containing 94,164 tuples. On average, each tuple with missing values corresponds to 1.38 target tuples. The missing information pertains to players' nationality and bowling style attributes.  
**Candidate Tuple Construction**: For tuples with missing values (i.e., incomplete tuples), we first create a candidate set by selecting tuples that may assist in imputing the missing values. Given the large number of tuples in the data lake, we utilize explicit information, such as matching the same subject ID or linking to the same entity in the original data. This helps us establish effective filtering rules to identify potential candidates for incomplete tuples.  
**Expert Annotation**: The final step is managing the target tuples for each incomplete tuple. For each tuple with missing values, we obtain the corresponding candidate set from the previous step. We present each tuple, along with its candidate tuples, to experts, who evaluate whether any of the candidates can fill at least one missing value in the tuple, thereby being identified as target tuples. This manual process is very time-consuming and requires specific domain knowledge, making attempts to automate it quite challenging. To reduce annotation costs, we primarily focus on cases where the candidate set contains 10 or fewer tuples.
## 3 Reliability-Based Topology Optimization  
In the service life of the engineering design, variations may inevitably exist in material properties and geometry, as well as external loads, due to the inherent uncertainty of nature. Therefore, the DTO, without considering these input uncertainties, may not reasonably represent the level of structural reliability. Thus, RBTO analysis can achieve the optimal topologies with the effects of uncertain variables explicitly considered through the reliability.
## 3 Results
### Validation of the Simulation Model with Common Coded Excitation Signals  
To validate the simulation model, coded excitation signals including pseudo noise sequences, Barker code sequences and chirp signals were empirically selected and evaluated with the simulation model.  
Two reference signals are considered: the default sine-stimulation with four periods (Figure 3) and the impulse-stimulation of the system. In the case of the impulse-stimulation, the transmitted waveform contains a single pulse with a width of 25 \(\upmu\)s. For these measurements the burst voltage is set to 7 V\({}_{\mathrm{PP}}\) to perform the tests, mainly in the linear operating range of the system.  
Table 1 shows a summary of results based on the evaluation of the relative height of the positive and absolute side maximum in simulations and experiments. The sine function results in 89% and 96% height of the positive and absolute side maximum in the CCF, respectively. The impulse-stimulation results in 71% and 90% relative height of the positive and absolute side maximum in the CCF. For both reference signals, experimental and simulation results are in good agreement with 99.7% up to 100% for the positive and absolute side maximum of the CCF compared to the ACF out of the simulation. The agreement between experiment and simulation for the impulse stimulation is expected because the signal itself is used to calculate the transfer function of the system.