esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Small Size 1D-QDFT Algorithms
### Algorithm 2, \(N=3\)  
Let \(\mathbf{x}_{3}=\left[x_{0},x_{1},x_{2}\right]^{T}\) and \(\mathbf{y}_{3}=\left[y_{0},y_{1},y_{2}\right]^{T}\) be three-entry input and output vectors, and \(x_{n}\in H,y_{m}\in H\), \(m,n=0,1,2\).  
The 3-point QDFT can be represented as  
\[\mathbf{y}_{3}=\mathbf{E}_{3}\mathbf{x}_{3}, \tag{4}\]  
where  
\[\mathbf{E}_{3}=\begin{bmatrix}1&1&1\\ 1&a_{3}&a_{3}^{*}\\ 1&a_{3}^{*}&a_{3}\end{bmatrix}\text{ is an order three QDFT matrix, }a_{3}=-0.5-0.5i-0.5j-0.5k,\text{ and symbol (*) denotes the conjugate of a quaternion number.  
The calculation of the QDFT under expression (4) is redundant. To calculate this expression, 4 quaternion-valued multiplications and 6 quaternion-valued additions are required. We will show how one can reduce the number of operations required when calculating a three-point QDFT.  
Let us perform the following decomposition:  
\[\mathbf{E}_{3}=\begin{bmatrix}1&1&1\\ 1&0&0\\ 1&0&0\end{bmatrix}+\begin{bmatrix}0&0&0\\ 0&a_{3}&a_{3}^{*}\\ 0&a_{3}^{*}&a_{3}\end{bmatrix}.\]  
Taking advantage of the structure of the matrices obtained as a result of decomposition, the three-point QDFT algorithm can be written as follows:  
\[\mathbf{A}_{4\times 3}=\begin{bmatrix}1&0&0\\ 1&1&1\\ 0&1&0\\ 0&0&1\end{bmatrix},\mathbf{W}_{4}=\mathbf{I}_{2}\oplus\mathbf{H}_{2}=\begin{bmatrix} 1&0&\mathbf{0}_{2}\\ 0&1&\\ \mathbf{0}_{2}&1&1\\ \mathbf{0}_{1}&1&-1\end{bmatrix},\mathbf{A}_{3\times 4}=\begin{bmatrix}0&1&0&0\\ 1&0&1&0\\ 1&0&0&1\end{bmatrix},\]  
\[\mathbf{D}_{4}=diag\left(1,1,s_{0}^{(3)},s_{1}^{(3)}\right),\]  
\(s_{0}^{(3)}=1/2\left(a_{3}+a_{3}^{*}\right)=-1/2\), \(s_{1}^{(3)}=1/2\left(a_{3}-a_{3}^{*}\right)=(-i-j-k)/2\),  
\(\mathbf{H}_{2}\) is (as we have already noted) the second-order Hadamard matrix, \(\mathbf{I}_{N}\) is the order \(N\) identity matrix, and \"\(\otimes\)\", \"\(\oplus\)\" mean the tensor product and direct sum of two matrices, respectively [30].  
Thus, we described an algorithm that requires 8 quaternion additions and only 2 trivial quaternion multiplications.  
Figure 2 shows a data-flow diagram of the proposed algorithm for the implementation of 3-point QDFT. To improve the readability of the figures, in Figure 2 (and in the other figures), we decided not to write superscripts for the entries \(s_{0}^{(3)}\) and \(s_{1}^{(3)}\) of the diagonal matrix \(\mathbf{D}_{4}\).
## 3 Experimental Data
### Hourly Change Coefficient of Outdoor Temperature \(\beta\) Modified
#### 3.2.1 Analysis of Simultaneity  
For the simultaneous occurrence of meteorological parameters, the calculation of fresh air load at the present stage is selected according to the single point value design calculated by the dry bulb temperature calculated by outdoor air conditioning, without considering the hourly changes in wet bulb temperature and wind speed at the same time; each outdoor parameter occurs at the same time, and the simultaneous occurrence characteristics of parameters are ignored in the separate analysis of each parameter. Therefore, the simultaneous occurrence of humidity and wind speed should be taken into account when selecting the dry bulb temperature data, so as to ensure the accuracy and rationality of outdoor meteorological parameter selection.  
Therefore, on the basis of the meteorological data of Beijing and Shanghai for 15 years previously selected, the \(\beta\) values of dry bulb temperature, wet bulb temperature, and wind speed were calculated using Formula (4). Since any two of the above three categories of meteorological elements are linear and continuous data, and any two categories of observations are paired and independent, showing normal distribution on the whole, satisfying the applicable conditions of the Pearson correlation coefficient. Therefore, the Pearson correlation coefficient is used to represent the degree of linear correlation among dry bulb temperature, wet bulb temperature, and wind speed. The calculation process is shown in Formula (5), and the results are shown in Figure 2. As can be seen from Figure 2, there is a strong correlation between dry bulb temperature and wet bulb temperature, a weak correlation between dry bulb temperature and wind speed, and a very weak correlation between wet bulb temperature and wind speed in Beijing and Shanghai.  
\[r=\frac{1}{n-1}\sum_{i=1}^{n}\left(\frac{X_{i}-\overline{X}}{\sigma_{X}}\right) \left(\frac{Y_{i}-\overline{Y}}{\sigma_{Y}}\right) \tag{5}\]  
Type: \(X_{i}\), \(Y_{i}\)--dry bulb temperature \(\beta\), wet bulb temperature \(\beta\), and wind speed \(\beta\), \(\frac{X_{i}-\overline{X}}{\sigma_{X}}\) --the standard fraction of the sample \(X_{i}\), \(\frac{Y_{i}-\overline{Y}}{\sigma_{Y}}\) --the standard fraction of the sample \(Y_{i}\), \(\overline{X}\)--sample mean of \(X_{i}\),\(\overline{Y}\)--sample mean of \(Y_{i}\), \(\sigma_{X}\)--sample standard deviation of \(X_{i}\), \(\sigma_{Y}\)--sample standard deviation of \(Y_{i}\).
# Cyber-Physical Systems for Water Supply Network Management: Basics, Challenges, and Roadmap
## 3 CPS Architecture and Applications
### CPS Functional Attributes
#### 3.2.1 Connection Level (C1)  
This level entails providing a seamless connection to the acquired sensor data from the physical environment. To provide a monitoring platform to critical infrastructure such as water supply systems, the data related to this infrastructure must be acquired in a secure way. Thus, in the first level of a CPS, error-free data must be collected. These data are acquired by the installed sensors. The sensors are arranged in sensor nodes to provide seamless connectivity and communications among the sensing elements. Communication protocols, therefore, play a vital role in this level to allow tether-free communication between sensor networks and for data transfer to the central server [10]. Just like the IoT-based technology, low-power communication protocols are requirements for most applications and used for this purpose. Most importantly, sensor selection is crucial at this level depending on the specific applications domain. For example, in the WSS application domain, such as in pipeline leakage detection and localization applications, pressure sensors, flow sensors, accelerometers (to measure vibration on a water pipe surface), ultrasonic sensors, and temperature sensors can be deployed for real-time measurements [4]. Likewise, pH and water quality sensors may be deployed for water quality monitoring applications. This task is accomplished by deploying the vast categories of sensors at the specific locations along water distribution networks for incessant measurement of water quality and leakage parameters. With CPS, the sensors can be coordinated with respect to their functionalities at this level.
# The LHAASO PeVatron Bright Sky: What We Learned
## 3 Overview of the 12 LHAASO Sources
### LHAASO J1929+1745  
The maximum energy of LHAASO J1929+1745 is estimated to be \(E_{M}\approx\) 700 TeV [43,131] and could be associated with two different TeV counterparts: 2 HWC J1928+177/HESS J1928+181 and 2 HWC J1930+188/HESS J1930+188/VER J1930+188, separated by about 1 degree (see Figure 11).  
2 HWC J1928+177/HESS J1928+181 has almost the same centroid as the LHAASO source and is an extended \(\gamma\)-ray emitter discovered by HAWC [117] and then confirmed by HESS [179] (VERITAS had no detection because of the large extension of the source [180]). The recent study by [181] hypothesized three different origins for the VHE/UHE emission detected, confirmed in the third catalog as 3 HWC J1928+178. Owing to the spatial correspondence with the young PSR 1928+1746, located at 0.03\({}^{\circ}\), the two most probable (the same as assumed by [179]) are IC emission due to electrons diffusing away from the PWN, and CR protons produced by the pulsar then interacting with a nearby MC. However, the estimated age of the PSR (\(\sim\)80,000 yrs) and the lack of X-ray detection in its correspondence open the possibility that 3 HWC J1928+178 could be another TeV halo. This possibility is also favored by the need to add a further extended component to spatially model the surroundings of this source [181]. A recent study [182] analyzed the affect of the PSR proper motion on the TeV halo morphology, estimating a formula for the maxi  
Figure 10: LHAASO J1908+621. (**Left**): radio map of the LHAASO J1908+621 region from [173] with the addition of LHAASO centroid and extension, roughly estimated from the map in [43]. The white solid lines are the VERITAS contours. (**Right**): one of the lepto-hadronic models developed in order to explain the SED of this source, where the contribution of hadrons at the highest energies is evident [Figure form [175]].  
mum possible offset depending on the \(\gamma\)-ray energies, PSR transversal velocity, and the distance. They concluded that, for E \(>\) 10 TeV, an off-set between a pulsar and an associated extended emission cannot be explained by PSR proper motion. In particular, in order to explain LHAASO J1929+1745 with a TeV halo from the PSR 1928+1746, we need a very high transversal velocity (\(>\)2700 km/s).  
The second source, 2 HWC J1930+188/HESS J1930+188/VER J1930+188, was detected by all the current IACTs as a point-like source [99; 117; 183], and it is strongly associated with the PWN in the 2000 yr old SNR G54.1+0.3 [131], with no possibility of understanding if the \(\gamma\)-ray emission is from the shell or from the PWN. This also has a point-like GeV no-pulsed counterpart detected by Fermi-LAT, 3 FGL J1928.9+1739 [131; 180]. The latest analysis by [181] confirmed the VHE steep spectrum detected by VERITAS, which could be a hint of the presence of a cutoff (too faint to be validated). If this cutoff is confirmed, this source could be ruled out as the origin of the UHE \(\gamma\)-ray emission detected by LHAASO.  
No neutrino constraints are present for this source so far.
## 3 Results and Discussion
### Removal of 4-CP by MPHAC, PS and the Combined MPHAC/PS System  
In order to clarify the synergistic effect of MPHAC and PS, a series of preliminary experiments were performed under the same conditions using MPHAC alone, PS alone, and the MPHAC/PS system. The obtained 4-CP removal factor is plotted in Figure 3 as a function of the contact time. It was found that PS alone was able to remove only 9.7 \(\pm\) 0.5 of 4-CP at a reaction time of 90 min. This low removal factor is due to the low activation of PS. As mentioned earlier, PS needs activators such as UV light, heat, or transfer metals, to activate and produce sulfate radicals. When using PS alone, the 4-CP removal mechanism is attributed only to the degradation process, which is not very efficient due to the inactivation of PS. On the other hand, when using MPHAC alone, the adsorption process is the predominant process for 4-CP removal. In this case, due to the high adsorption capacity of the used catalyst, a higher removal factor of 4-CP up to 49.7 \(\pm\) 2.2% was achieved at a reaction time of 90 min. However, the combined MPHAC/PS system exhibited the highest 4-CP removal factor, 79.1 \(\pm\) 3.5%, at a reaction time of 90 min. This system removes a high percentage of 4-CP from the beginning of the reaction, 47.8 \(\pm\) 2.4% after 2 min, indicating that MPHAC is a highly efficient catalyst in the Fenton-like process. Simultaneous adsorption and degradation processes are involved in this system, resulting in an effective removal of 4-CP. The Fe\({}_{3}\)O\({}_{4}\) particles on the catalyst surface lead to the activation of PS and the subsequent degradation process. As a result, the MPHAC/PS system was used in the following experiments and the optimized parameters for an efficient 4-CP removal were determined. [PERSON] et al. [36] prepared nano Fe\({}^{0}\)-loaded superfine powdered activated carbon for synergistic adsorption and PS activation to remove carbamazepine (CBZ) from aqueous solution. It was observed that CBZ could not be degraded by PS alone. However, a higher removal efficiency of CBZ was obtained for Fe\({}^{0}\)-loaded activated carbon in the presence of PS, suggesting that the removal efficiency was improved by adding PS to the reaction.
# The Analysis of WJ Distribution as an Extended Gaussian Function: Case Study
## 3 Results and Discussion
### Method II
#### 3.2.3 An Account of the Differences between Methods I and II  
We give conceivable reasoning for the different outcomes between Methods I and II. First of all, it is noticeable that the parameter \(\alpha=1.188\) in Method II is much smaller than \(\alpha=12.52\) in Method I, whereas the corresponding \(\beta=0.9522\) is much larger than \(\beta=0.07915\). In its deduction, the WJ function has assumed that a series of random variables follow exponential distributions  
\[f(x_{n})=\frac{\alpha+(n-1)\beta}{e}e^{-[\alpha+(n-1)\beta]x_{n}}(n=1,\,2,\,3,\,\ldots).\]  
when \(\beta\) is far smaller than \(\alpha\), both the means and variances of the first several random variables may be considered roughly equal (and are the main contributing terms to the probability). The observation, in essence, satisfies the assumptions of the Gaussian function, and thus, the outcome of Method I discloses better characteristics in modeling the Gaussian function than that of Method II.  
Referring to the computation above, either of the two expansion methods has distinct advantages itself. In Method I, the WJ distribution fits the Gaussian function much more effectively in an accurate and self-consistent manner. In Method II, the WJ distribution obtained exhibits unsatisfactory performance to fit the Gaussian function, but the expression of \(\sigma\) estimation is simpler, and we may conveniently derive the parameter values of the WJ function for fitting the Gaussian distribution or as initial inputs for searching more accurate solutions.
# Power Quality Fault Identification Method Based on SDP and Convolutional Neural Network
###### Abstract  
The global power demand is increasing year by year. Power quality fault recognition plays an important role in determining the fault type when a fault occurs, so as to maintain power quality and supply stability. Therefore, this paper proposed using the symmetrized dot pattern (SDP) algorithm plus the convolutional neural network (CNN) to study power quality fault recognition. Six common power quality fault models were taken as the subjects of discussion, including normal voltage, voltage sag, voltage swell, voltage interruptions, voltage flickers, and voltage harmonics. First, the voltage variation data of five cycles were extracted from a 60 Hz power supply and introduced into the SDP. Afterwards, the data were converted into graph data, which could be used for fault recognition. Lastly, the power quality fault type was identified by CNN. In this study, 600 random fault data (100 data per faulty were imported into the algorithm, and the recognition rate reached as high as 92.9%. Additionally, SDP could reduce the mass original data. After the subtle changes in the output signals were captured, they could be observed by images. The power quality fault state could thus be accurately recognized by CNN.  
power quality; symmetrized dot pattern; convolutional neural network +
Footnote †: journal: Applied Sciences  
to identify similar waveforms, classified the voltage and current waveform images, and provided information on possible faults. Ref. [11] developed a combined power quality automated recognition method based on Hilbert-Huang Transforms (HHT) and Relevance Vector Machine (RVM). The HHT can efficiently process harmonic signals and extract the features of power quality signals, whereas the RVM requires a large number of samples to perform classification and recognition according to power quality characteristics.  
This study proposed a hybrid algorithm based on SDP and convolutional neural networks (CNN) for power quality fault recognition for rapid classification and recognition. Besides the signal of normal voltage, this study also included instantaneous signals of five common power quality accidents, such as voltage sag [12; 13], voltage swell [13], voltage interruption [13], voltage flicker [14], and voltage harmonic [15] for experimental testing. First, a polar diagram was plotted based on the original power signal in the SDP for fault recognition [16; 17; 18]. Then, the CNN performed recognition to obtain the fault type. As the SDP was highly sensitive to input signals, a subtle change in the input signals is reflected in the output image. Meaningful eigenvalues can be extracted from the fault signals. Additionally, the proposed method was compared with HOG+SVM, HOG+Ensemble, and HOG+KNN to verify its accuracy.
# Dynamic Voltage Optimization Based on In-Band Sensors and Machine Learning
## 3 Case Studies and Issues for Conventional CVR
### Summary  
As described in the brief case studies contained here, it is clear that CVR and related approaches to voltage optimization are effective even when implemented non-optimally, addressing only a minority of feeders, and in the absence of a decision framework. The cases presented here vary by operational characteristics as well as use (conservation or peak reduction), but in all cases, significant saving was achieved and service degradation was avoided. Additionally, research activity related to CVR and similar approaches is active worldwide, from small-scale pilot studies using voltage regulators in the U.S. in 2010 [33] to studies on the concept of an \"active distribution grid\" in China in 2016 [34] and reactive power compensation schemes in Europe in 2017 [35].  
Regardless of the decision frameworks employed and/or the approaches taken to implement a voltage optimization solution, the percentage of addressable feeders remains very low due to construction and condition. Furthermore, as summarized succinctly in [14], the \"physics of the processes are very complicated\" which results in several misunderstandings of the causes, effects, and outcomes. Additionally, due to highly variable distribution loads, the conservation factor (CVR\({}_{f}\)) is neither constant nor linear, and widely held assumptions may be incorrect [14]. As a result, the benefits of additional data collection on systems under test is necessary.  
In each of these cases, the conclusions are similar: the connection between distribution efficiency and voltage optimization is direct, and the savings due to deployment of such approaches in the distribution grid is likely to outweigh the cost of deployment, particularly over time. However, direct monitoring of voltage levels in the distribution grid requires deployment of sensors, communication schemes to retrieve information in-band, and algorithms to process, interpret, and react to grid conditions. In contrast, the architecture proposed here, which leverages already-deployed \"smart meters\" to provide information to upstream systems for geospatial and schematic location refinement, can be leveraged alongside CVR systems to implement a richer form of \(\overline{\text{DVO}}\).
# The Effect of Green Areas on Urban Microclimate: A University Campus Model Case
## 3 Results and Discussion  
### Seasonal Variations, the Impact of Green Areas, and Microclimate Improvement Potential  
The study has identified that vegetative areas (broadleaved trees, coniferous trees, shrubs, and vines) play a crucial role in regulating [72,73] the urban microclimate [23,33,34,74].  
January (winter season): During winter, surface temperatures in vegetative areas were measured between 2 and 5 \({}^{\circ}\)C, demonstrating that green areas maintain their thermal regulation effect throughout the year.  
April (spring season): In spring, surface temperatures in vegetative areas ranged between 20 and 30 \({}^{\circ}\)C. The shading effect of broadleaved trees was particularly evident in this period.  
August (summer season): In summer, surface temperatures in green areas were recorded between 20 and 30 \({}^{\circ}\)C. Broadleaved trees and shrubs stood out for their strong shading properties.  
October (autumn season): During autumn, surface temperatures in vegetative areas ranged between 15 and 25 \({}^{\circ}\)C, further confirming that green areas have a year-round temperature-regulating potential. It has been determined that green areas exhibit temperature regulation functions in all seasons [75,76,77], particularly mitigating rising temperatures in summer [36,78,79] and reducing the urban heat island effect [80,81,82]. This finding under scores the necessity of prioritizing the preservation and expansion of green areas in urban planning processes.
###### Abstract  
In the intelligent fault diagnosis of rotating machinery, it is difficult to extract early weak fault impact features of rotating machinery under the interference of strong background noise, which makes the accuracy of fault identification low. In order to effectively identify the early faults of rotating machinery, an intelligent fault diagnosis method of rotating machinery based on an optimized adaptive learning dictionary and one-dimensional convolution neural network (1 DCNN) is proposed in this paper. First of all, based on the original signal, a redundant dictionary with impact components is constructed by K-singular value decomposition (K-SVD), and the sparse coefficients are solved by an optimized orthogonal matching pursuit (OMP) algorithm. The sparse representation of fault impact features is realized, and the reconstructed signal with a concise fault impact feature structure is obtained. Secondly, the reconstructed signal is normalized, and the experimental dataset is divided into samples. Finally, the training set is input into the 1 DCNN model for model training, and the test set is input into the trained model for classification and detection to complete the intelligent fault classification diagnosis of rotating machinery. This method is applied to the fault diagnosis of bearing data of Case Western Reserve University and worm gear reducer data of Shanghai University of Technology. Compared with other methods and models, the results show that the diagnosis method proposed in this paper can achieve higher diagnosis accuracy and better generalization ability than other diagnosis models under different datasets.  
k-singular value decomposition (K-SVD); orthogonal matching pursuit (OMP); one-dimensional convolutional neural network (1 DCNN); feature extraction; feature classification +
Footnote †: journal: Medical and Electrical Engineering Institute, Zhengzhou University of Light Industry, 5 Dongfeng Road, Zhengzhou 450002, China; [EMAIL_ADDRESS] (H.W.); [EMAIL_ADDRESS] (W.D.)  
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