esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Method
### Highest Frequency Region  
In principle, the method to calculate the impulse response for frequency region f3 equals the method described in Section 2.3. For frequencies over 4 kHz the radiation method only synthesizes the desired sound field at the discrete listening points, but not in between [49]. For human listeners, neither the exact frequency nor the phase are represented in the auditory pathway ([39] Chapter 3). Amplitude of such high frequencies mainly contributes to the perception of brightness and the phase contributes to the impulsiveness of attack transients. Both are important aspects of timbre perception ([53] Chapter 11), ([39] Chapter 2).  
Therefore, it is adequate to approximate the desired amplitudes and phases at discrete listening points by solving Equation (18). The difference between frequency regions f2 and f3 is the selection of optimal listening points. Instead of choosing the impulse response with minimum signal energy, Equation (20), the ideal impulse response for f3 is the shortest, because it exhibits the highest impulse fidelity. When convolved with a short impulse response, the frequencies of source signals stay in phase. Quite contrary, long impulse responses indicate out of phase relationships. Phase is mostly audible during transients. Consequently, the shortest impulse response is ideal, because it maintains the characteristic, steep attack transient of harpsichord notes.  
Impulse responses for one loudspeaker and three different microphone array locations \(v\) is illustrated in Figure 12. The shortest impulse response can be identified visually.  
Figure 12: Three exemplary impulse responses \(R^{{}^{\prime}}_{\Omega,v}\). Here, \(t\) is the time in seconds and \(u\) is the voltage to drive the loudspeaker. The shortest out of \(V=11\) impulse responses is ideal, as it exhibits the best impulse fidelity. It can be identified visually.
## 3 LDPC Coding--Preliminaries
### LDPC Codes  
The binary parity check matrix \(\mathbf{H}\) of a binary \((N,K)\) LDPC code has a size \(M\times N\), where \(N\) is the length of the code vector (encoded message), \(K<N\) is the length of an information vector, and \(M=N-K\) represents the number of parity bits in the code vector. The measure of coding redundancy is the code rate, defined as \(R=K/N\). In the systematic encoding process, \(M\) parity bits are added to the information vector \(\mathbf{u}=\{u_{1},u_{2},\ldots,u_{K}\}\), forming the code vector \(\mathbf{c}=\{c_{1},c_{2},\ldots,c_{N}\}\). All vectors are binary, and it is convenient to consider them to be defined over the Galois finite field \(\mathrm{GF}(2)\).  
In the decoder, the data received \(\mathbf{y}\), which is a vector of length \(N\), are corrupted by channel imperfections. If--and only if--the hard decisions made from \(\mathbf{y}\) meet the parity check equation, it will be recognized as a correct, (error-free) vector \(\mathbf{\hat{c}}\). The parity check equation is expressed as \(\mathbf{H}\mathbf{\hat{c}}^{T}=\emptyset_{M\times 1}\), where the matrix operation is in \(\mathrm{GF}(2)\) arithmetic. If the equation is not satisfied, the iterative decoding algorithm is started that can correct errors, with soft decisions as the algorithm input, which are usually in the form of log-likelihood ratio (LLR) vector:  
\[LLR(\mathbf{c}|\mathbf{y})=\bigg{[}\log\frac{P(c_{1}=0|\mathbf{y})}{P(c_{1}=1 |\mathbf{y})},\log\frac{P(c_{2}=0|\mathbf{y})}{P(c_{2}=1|\mathbf{y})},\cdots, \log\frac{P(c_{N}=0|\mathbf{y})}{P(c_{N}=1|\mathbf{y})}\bigg{]}. \tag{1}\]  
Alternatively to matrix representation, LDPC codes can also be represented by the associated bipartite Tanner graph [3; 31]. In the Tanner graph, the variable nodes (VN) representing codeword bits are associated with columns of \(\mathbf{H}\) and the check nodes (CN) representing parity checks are associated with rows of \(\mathbf{H}\). The nonzero elements in \(\mathbf{H}\) correspond to edges in the code graph. This means that the graph nodes are divided into two distinct sets (VNs and CNs), and that the edges only connect two different types of nodes. For an \((N,K)\) LDPC code with block length \(N\), the graph consists of \(N\) variable nodes and \(M=N-K\) check nodes.  
The most popular LDPC decoding algorithms are all algorithms involving belief propagation (BP), including sum product (SPA) algorithms and several simplified versions operating on LLR beliefs, such as min-sum algorithms (MSA), offset min-sum algorithms (OMSA), or normalized min-sum algorithms (NMSA) [32; 33; 34].  
Since the message length in our assumed IoT system is assumed to be relatively short, the block lengths of codes that are considered and investigated in this article are short-to-medium. We investigate such codes constructed with a developed procedure that will be briefly presented in one of the next sections.
# RCM: A Remote Cache Management Framework for Spark
## 5 Experimental Results and Analysis
### Performance Result
#### 5.2.3. RCM Performance Improvement  
CREP and CPL have been tested individually in Sections 5.2.1 and 5.2.2. This section verifies the effectiveness of RCM, which consists of CREP and CPL. Firstly, advanced cache management strategies of Spark, including MCM [25], SACM [21] and DMAOM [24], are tested to compare the performance of RCM.  
The execution time of benchmark jobs, PageRank, K-means and WordCount, are shown in Figure 8a-c, respectively. For Web-Google in Figure 8a, RCM gives a 28% improvement of PageRank job at most. For Cit-Patents in Figure 8b, RCM gives a 42.1% improvement of K-means job at most. For Web-BerkStan in Figure 8c, RCM gives a 16.8% improvement of the WordCount job at most. The results of the three jobs confirm that RCM has the most obvious optimization compared with the other strategies. However, RCM has less optimization on computation-intensive jobs. In Figure 8c, the execution time of three datasets shows that RCM is not in a dominant position. For Cit-Patents in Figure 8c, RCM does not perform as well as MCM does. Analyzing this phenomenon to reveal the best type of scenario in which RCM can achieve the most optimization, we can make a classification according the frequency of accessing memory. PageRank and K-means are input and output intensive for cache resources, while WordCount is not. The job type of input and output intension has obvious acceleration by optimizing cache management; this is why RCM has a dominant position in PageRank and K-means. On the contrary, WordCount is less affected by cache management optimization. It is difficult to reduce the execution time from the view of optimizing cache management. In the end, we conclude that the best type of scenario in which RCM can achieve the most optimization is an input and output-intensive scenario, which is accessing cache resource frequently.  
Figure 7: Job execution time.
# Light-Weighted Password-Based Multi-Group Authenticated Key Agreement for Wireless Sensor Networks
## 4 Security Analysis
### Resistance to Undetectable On-Line Password-Guessing Attacks  
In the proposed protocol, an adversary \({SN_{i}}^{*}\) cannot compute the correct \(C_{i}=h(DID_{i}\|h(ID_{i}\|)\)\(\|{K_{1}}\|{X_{i}}\|{T_{i}})\) without \({SN_{i}}\)'s identity \(ID_{i}\), where \(K_{1}=T_{r_{i}}(pk_{S})\)\(mod\)\(p\), \(DID_{i}=K_{1}\oplus ID_{i}\), \(X_{i}=T_{r_{i}}(x)\)\(mod\)\(p\) and \(T_{i}\) is the timestamp, and so such an adversary fails to send out \(M_{i,1}=\{DID_{i}\), \(X_{i}\), \(C_{i}\), \(T_{i}\}\) in Step 1. Additionally, \({SN_{i}}^{*}\) who has \(ID_{i}\) and is disguised as \({SN_{i}}\) guesses a password \({pw_{i}}^{*}\), computes \({C_{i}}^{*}=h(DID_{i}\|Q_{i}\|K_{1}\|X_{i}\|T_{i})\), where \(Q_{i}\)\({}^{*}=h(ID_{i}\|pw_{i})\) and sends \(M^{{}^{\prime}}_{i,1}=\{DID_{i}\), \(X_{i}\), \(C_{i}\)\({}^{*}\), \(T_{i}\}\) to \(S\) in Step 1. After receiving \(M^{{}^{\prime}}_{i,1}\), \(AS\) will detect this failed password-guessing by checking \(C_{i}=h(DID_{i}\|Q_{i}\|K_{1}\|X_{i}\|X_{i}\|T_{i})\) in Step 2, where \(K_{1}=T_{mk}(X_{1})\)\(mod\)\(p\), \(ID_{i}\)\({}^{{}^{\prime}}=DID_{i}\oplus\)\({\rm K_{1}}^{{}^{\prime}}\), \(HID^{{}^{\prime}}_{1}=h(ID^{{}^{\prime}}_{i}\|mk)\), \(Q_{i}=h(ID_{i}\|pw_{i})\). Therefore, the proposed protocol is secure against undetectable on-line password-guessing attacks.
# An EV SRM Drive and Its Interconnected Operations Integrated into Grid, Microgrid, and Vehicle
## 4 G2V/V2G Operations
### M2V/V2M Operations
#### 5.2.2 Measured Results  
(a) M2V operation  
In the M2V operation, the microgrid DC-link voltage \(V_{dc}=400\) V is established by the SRG and interleaved boost converter. The measured results of the M2V operation under battery charging power \(P_{b1}\approx 500\) W are shown in Figure 32. A successful charging operation can be seen in the results.  
(b) V2M operation  
In the V2M operation, through the CLLC resonant converter on the EV, the onboard battery voltage is boosted to establish the microgrid common DC bus voltage. Then, it can supply the DC load in the microgrid and charge the battery in the BESS system. The measured results of the V2M operation under DC load power \(P_{L}\approx 500\) W and battery charging power \(P_{b3}\approx 500\) W are shown in Figure 33. A successful discharging operation can be observed from the results.  
Summary: After establishing that the developed EV SRM drive has various interconnect functions, the operating characteristics of all modes are verified by a lot of measured results, which are briefly summarized as follows:  
1. The battery directly powered basic EV SRM drive with \(v_{dc}=500\) V is first verified, including speed dynamic response with defined specifications, the effect of DCT in enhancing the winding current response, the acceleration/deceleration, the reversible running, and the regenerative/dynamic braking operations.
2. The EV SRM drive is powered by battery via interleaved bidirectional interface converter. The fixed and varied DC-link voltages boosted from battery are used to power the motor drive, and their comparative performance is evaluated. The loss-consumed battery energies under the programed speed patterns yielded by various DC-link voltage approach are demonstrated, as shown in Figures 12 and 13.
3. The G2V/V2G operations of the developed EV SRM drive are achieved by a CLLC resonant converter and a bilateral 3P3W inverter. The normal operations and satisfactory performance under G2V mode can be seen in Figures 19 and 20. Regarding the V2G operation, the results presented in Figures 21-23 demonstrate that the preset real and reactive powers are successfully sent back to the utility grid.  
Figure 33: Measured results of V2M operation under DC load power \(P_{L}\approx 500\) W and battery charging power \(P_{l\delta}\approx 500\) W: (**a**) \((v_{b},\,i_{b})\) of EV battery; (**b**) \((v_{gs},v_{ds},i_{Lp},i_{Ls})\) of CLLC resonant converter; (**c**) \((v_{dc},v_{b3},i_{b3})\) of battery in DC microgrid.  
* V2V/M2V/V2M operations: Figures 27-29 verify the normal V2V operations under different charge current patterns. And the successful M2V/V2M operations can be observed from the results depicted in Figures 32 and 33.
# Research on Improving Satellite Positioning Precision Based on Multi-Frequency Navigation Signals
## 5 Discussion  
This paper introduces a method of using multi-frequency navigation signals to improve positioning precision and uses the observation data of the CAPS system and GNSS system to conduct simulation experiments. Statistical analysis was carried out on the  
Figure 8: Positioning precision and improvement of multi-frequency navigation signals in CAPS system and GNSS system. Subfigures (**a**) at left column are standard deviation and the root mean square error in three directions and root mean square error of three-dimensional space change with the number of downlink frequencies in CAPS system; abd subfigures (**b**) at right column are standard deviation and the root mean square error in three directions and root mean square error of three-dimensional space change with the number of downlink frequencies in GNSS system.  
experimental results in terms of root mean square error, improvement percentage, and standard deviation. It is verified that the method can improve the positioning precision, improve the stability of the positioning results, and can effectively reduce the PDOP value. The method of improving the positioning precision in this paper is different from the study of multi-scale ionospheric irregularity proposed in the paper [5]; it is also different from the improvement of ionospheric model in the paper [6]; it is also different from the improved solution of the whole week ambiguity proposed in paper [7]. Instead, the multi-channel navigation signal is received by the multi-frequency receiver, the distance from the satellite to the receiver is measured, and multiple pseudo-distance equations are established to achieve the purpose of positioning precision. This paper analyzes the improvement degree of multi-frequency on positioning precision from the time domain, and quantitatively analyzes the positioning results according to the number of downlink frequencies, which is different from the analysis of the influence of multi-frequency on the positioning results from the frequency domain in [25]. It is different from the one proposed by paper [8] to optimize satellite constellation from the angle of altitude angle to achieve the purpose of improving the PDOP value. It is also different from the weighted PDOP value method proposed in the paper [26] as the basis for selecting the best constellation. By increasing the number of downlink navigation signals of a single satellite without increasing the number of satellites, the method effectively improves the spatial layout of satellites and reduces the PDOP value. Through theoretical research and simulation analysis, it is found that when each satellite sends down multiple navigation signals, the ratio of the improved PDOP to the original PDOP is inversely proportional to the square root of the number of downlink navigation signals.
## 5 Conclusions  
We analyzed the correlation of MODIS NDVI, EVI, LAI and FPAR products with short-term and annual GPP measured at eddy covariance flux towers. Our results show that EVI is useful for the analysis of short-term variations in tower-estimated GPP (_i.e._, at the time scale of weeks to months) in agreement with other studies. Some diagnostic models started to utilize the EVI-GPP seasonal correlation, but caution should be exercised when mapping geographic patterns of GPP using EVI, because the EVI/GPP relationship is not constant across forest types. In our study, annual mean MODIS LAI was the best metric for estimation of the spatial patterns of annual GPP (GPP in g C/m\({}^{2}\)/year = \(617\times\text{LAI}-376\)). Annual mean NDVI and EVI were less correlated to tower-estimated GPP, but annual mean NDVI was a better predictor than annual mean EVI of annual GPP in non-tropical areas, suggesting that the GPP/EVI ratio is site specific. Extrapolating the relationship between annual mean LAI and annual GPP at the tower sites to forests globally produced reasonable GPP estimates that were also consistent with known climatic influences on GPP. The results allowed the rediscovery of the ground measured LAI and tower-estimated GPP relationship, as explained by strongly interrelated biophysical processes and the functional convergence hypothesis.  
There are several concerns related to the uncertainty of MODIS products. These analyses were based on MODIS products, and different satellite products may be not able to get such high correlation between annual mean LAI and annual GPP. For the uncertainty caused by different resolution analysis, we minimized the influence on GPP estimate by carefully choosing the dataset not to include contaminated or heterogeneous data.  
We sincerely appreciate the help of the researchers affiliated with the FLUXNET program. This study was funded by NASA's Earth Sciences Program. We are grateful to [PERSON] and [PERSON] for useful comments.
## 2 Related Works
\begin{cases}1&\text{if }\mathbf{x}_{i}\in N_{K}(\mathbf{x}_{ j})\text{ }or\mathbf{x}_{j}\in N_{K}(\mathbf{x}_{i});\\ 0&\text{otherwise},\end{cases}  \tag{4}\]  
where \(N_{K}(\mathbf{x}_{i})\) denotes the set of \(K\) nearest neighbors of \(\mathbf{x}_{i}\). From the weight matrix \(\mathbf{W}\), it is easy to get its Laplacian matrix \(\mathbf{L_{W}}\) with the following element:  
\[\mathbf{L_{W}}(i,j)= \begin{cases}-\mathbf{W}(i,j)&\text{if }i\
eq j;\\ \sum_{l\
eq i}\mathbf{W}(i,l)&\text{otherwise}.\end{cases}  \tag{5}\]  
The Laplacian matrix is looked at as a graph which represents the pairwise relation of the data. Intuitively, if two data points \(\mathbf{x}_{i}\) and \(\mathbf{x}_{j}\) are close in the intrinsic geometry of raw data, their data representations should preserve this property. So it is natural to combine the sparse in (1) or low rank constraint in (2) with the Laplacian graph constraint in (3) to obtain new clustering models. Thus, the LapLRR method [15] presented a manifold regularization for LRR characterized by adopting the Laplacian graph constraint as follows:  
\[\min_{\mathbf{Z}}\lVert\mathbf{X}-\mathbf{XZ}\rVert_{F}^{2}+\lambda_{1} \lVert\mathbf{Z}\rVert_{*}+\frac{\lambda_{2}}{2}tr(\mathbf{ZL_{W}}\mathbf{Z}^ {T})\text{ s.t. }\mathbf{Z}\geq\mathbf{0}, \tag{6}\]  
where \(\mathbf{Z}\geq\mathbf{0}\) demands the elements of \(\mathbf{Z}\) being nonnegative. Considered that there usually exist noise and outliers in the raw data, the NSLLRR method [17] adopted sparse constraints both for the data reconstruction error and the data representation. Additionally, the method exploits the Laplacian matrix construction methods in different cases, which results a hyper Laplacian matrix even when the data sampling is insufficient. The NSLLRR model is shown as follows:  
\[\min_{\mathbf{Z}}\lVert\mathbf{X}-\mathbf{XZ}\rVert_{1}+\gamma\lVert\mathbf{ Z}\rVert_{*}+\lambda\lVert\mathbf{Z}\rVert_{1}+\beta tr(\mathbf{ZL}^{h} \mathbf{Z}^{T}), \tag{7}\]  
where \(\mathbf{L}^{h}\) is the hyper Laplacian matrix and \(\lambda\), \(\beta\) and \(\gamma\) are penalty parameters for balancing the regularization terms. In the above methods, the Laplacian graph constraint is generally constructed from the raw data samples under certain metric, which is easily interfered by the outliers or noise exiting in the original dataset. In this paper, we adopt a new strategy by using the manifold learning method to learn the underlining non-linear structure of the data and use the learned relation of the data to regularize the sparse and low rank representations. The manifold learning methods have been proved successful in representation of high dimensional data and robust to noise [24; 25], thus it is considered more suitable to reveal the non-linear property of high dimensional data for clustering, and expected to obtain better clustering performance. In the following section, we will give the proposed methods in detail.
## 3 Selected Research Results
### Application of Kurtosis  
Crack propagation leads to random kinematic nodes and additional degrees of freedom, loss of function, and safety hazard. The following equation can describe the extent of fatigue development of the cracking of the nozzle nose:  
\[D=C_{1}\left(1-\frac{S(\tau)}{S(\tau_{0})}\right) \tag{12}\]  
where \(C_{1}=\frac{S(\tau)}{S(\tau)-S(\tau_{f})}\) is used to determine the scaling factor, \(S(\tau)\) is the diagnostic symptom for operation time \(\tau\), \(S(\tau_{0})\) is the diagnostic symptom for operation time \(\tau_{0}\), and \(S(\tau)\) is the diagnostic symptom at the instant of nose breakage \(\tau_{f}\). The difference in the width of the energy-relevant band can be determined in the form of the spectral width factor to assess the damage development [6]:  
\[\
u=\frac{m_{2}^{2}}{m_{0}m_{4}} \tag{13}\]  
where the spectral moments were calculated from the following formula:  
\[m_{k}=\int_{-\infty}^{\infty}f^{k}S(f)df \tag{14}\]  
where \(S\) is the measurement signal.  
\[m_{0}=\int_{-\infty}^{\infty}S(f)df \tag{15}\]  
\[m_{2}=\int_{-\infty}^{\infty}f^{2}S(f)df \tag{16}\]  
\[m_{4}=\int_{-\infty}^{\infty}f^{4}S(f)df \tag{17}\]  
Figure 9: The time-frequency analyses for cylinder No. 1 in a suitable state (**a**) and for cylinder No. 5 with damage propagation (**b**).  
The kurtosis \(K\) is the fourth-degree moment equals:  
\[K=\frac{\frac{1}{n}\sum_{i=1}^{n}\left(s_{i}-s\right)^{4}}{\sigma^{4}} \tag{18}\]  
where \(\sigma\) is the standard deviation.  
Kurtosis measures the concentration of the results and determines how concentrated they are around the mean. Kurtosis was used because, according to [PERSON] [6], kurtosis changes occur in the first low-energy phases of damage development. The values of kurtosis presented in the figure for individual cylinders of the tested engine did not indicate damage. For cylinder No. 5, the kurtosis values were between the limit values defining the suitability state, and for cylinder No. 1 was too low (Figure 10).  
The kurtosis value in Figure 10 below the lower limit corresponds to the deregulation (reduction) in the injector opening pressure and lower value combustion pressure. This occurred for kurtosis, which is not the best diagnostic parameter.
# Aerospace
## 9 Additional Mission Concepts
### Venus Radar Mapping Constellation  
As with imaging satellites, small, low-cost Synthetic Aperture Radar (SAR) satellites such as that operated by ICEYE (\(\simeq\)100 kg) offer unprecedented radar imagery of the Earth's surface [51]. Such a radar constellation can greatly enhance our understanding of the Venusian surface and interior. While there are challenges, e.g., the download of large volume SAR data from Venus, on-board data processing techniques, such as those used by VERITAS [52] and deployable high-gain antennae, may be able to solve these problems [53]. An ADEPT entry system weighing approximately 300 kg can deliver a 150 kg small radar satellite to Venus orbit. The proposed VERITAS mission has a launch mass of 1500 kg. Such a future Discovery-class mission can deliver about five of these small satellites to low-circular orbits in a single launch and establish an SAR constellation at Venus.
## 6 Limitations and Future Work  
The proposed 3D microgestures are one-handed, but some users may prefer two-handed gestures [23,42]. Additionally, due to the Covid-19 pandemic, this study was conducted online preventing close observation of user habits while they assigned gestures to commands. If participants had actually used a VR or AR HMD during the experiment, they may have preferred different microgestures. Even though participants were encouraged to design different microgestures during the study, only two different microgestures were proposed. A face-to-face study rather than an online study might have led to more new gestures being proposed by participants. Although great care was taken to create a list of commands that would be representative of the commands users may perform while using VR or AR devices, it is possible that some important commands were excluded from this study.  
People with cognitive or motor disabilities may have difficulty performing microgestures. Video capture systems and machine learning could be used to recognize the microgestures and intentions from individuals with disabilities and may improve their ability to interact with computers, especially if they have difficulty using hand-held controllers or other input devices. Further evaluation of this microgesture set with qualitative and quantitative measurements of comfort, efficiency, accessibility, and acceptability is essential. Microgestures should be compared to other modes of input (e.g., hand-held controller) on productivity, error, comfort, and other usability factors when subjects use VR/AR. It is likely that the acceptability of gesture input will be different for VR vs. AR.