esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Cyclone Spinning Effect with Different Guide Vane Heights
## 2 Materials and Methods  
### Cyclone Structure  
A swirler, as a device for generating swirl flow, mainly consists of two parts: the pipe wall and the attached vanes. In this study, the inner radius of the swirler pipe wall is taken as R = 50 mm, and the length is taken as 550 mm. Three guide vanes are evenly spaced along the inner wall of the swirler, with a total length of 400 mm, 50 mm away from the swirler's inlet section and 100 mm away from the outlet section. Among them, the straight section of the guide vane is 100 mm long, and the curved section is 300 mm long. The curved section of the guide vane twists by 1\({}^{\circ}\) every 20 mm, the guide vane angle is 15\({}^{\circ}\), and the thickness of the guide vane is 5 mm. To explore the impact of changes in guide vane height on the hydraulic characteristics of the swirler, this paper takes the guide vane height H as a single variable. When selecting the guide vane height, a vane that is too high will affect the flow capacity of the pipe; a vane that is too low will result in an inconspicuous swirling effect. Within the appropriate range of guide vane heights, guide vane heights of H = 20 mm, 25 mm, 30 mm, 35 mm, and 40 mm are selected; that is, swirlers with relative guide vane heights H/R = 0.4, 0.5, 0.6, 0.7, and 0.8 are studied. The arrangement of the swirler guide vanes and guide vane parameters are shown in Figure 1.
## 3 Results  
### General Characteristics of the Participants by Category of Serum 25(OH)D Concentration  
As shown in Figure 2, the median serum level of all participants was 15.4 ng/mL. Men and women had median serum 25(OH)D levels of 16.4 ng/mL and 14.7 ng/mL, respectively, indicating that the overall serum 25(OH)D level was deficient (below 20 ng/mL) and the range of serum level was relatively broad.  
The proportion of participants with vitamin D deficiency (<10 ng/mL) and insufficiency (10 to <20 ng/mL) were 13.4% and 61.9%, respectively. The general features of the participants are listed in Table 1 according to categories of their serum 25(OH)D levels. In total, 46.1% of the participants with serum 25(OH)D levels below 10 ng/mL were male, whereas 53.9% were female. Furthermore, 55.8% of men and 44.2% of women had 25(OH)D concentrations of 10 to <20 ng/mL, and 64.8% of men and 35.2% of women had 25(OH)D concentrations greater than 20 ng/mL, indicating that women have significantly lower serum vitamin D levels than men. Higher 25(OH)D levels are associated with an older mean of age in both men and women.  
Regarding the educational level, the majority of participants were high school and college graduates; however, their serum levels were relatively lower than those of elementary and middle school graduates. As expected, physical workers had significantly higher serum concentrations than office workers and the unemployed. The residential areas had a significant impact on the serum 25(OH)D concentration, with residents of the southern areas having higher levels than those of the northern areas. These phenomena were more obvious in men. Current smoking, season variation, and physical activity had no effect on the 25(OH)D concentration in the serum. However, the serum 25(OH)D concentrations differed significantly depending on the current alcohol consumption. The level of serum 25(OH)D was significantly and favorably elevated by dietary supplements, especially in women. Dietary supplement use was, therefore, included as a covariate in the statistical models, as it is known that individuals' supplement use is associated with their diet, lifestyle, and health condition [42].  
Figure 2: Distribution of serum 25-hydroxyvitamin D (25(OH)D) concentration of participants. Median is represented by the line in the box. The interquartile range box represents the middle 50% of the distance (Q3–Q1) between the first and third quartiles (25 th percentile and 75 th percentile). The connecting lines of the boxes are the minimum and maximum values.
# Coverage, Rate, and Last Hop Selection in Multi-Hop Communications in Highway Scenarios
## 3 System Performance Analysis
### Communications Distances and Attained Rates  
By following the propagation measurements in [23], the maximum communication distance with tolerable received signal power \(\sigma\) for path \(\zeta\in\{L,R,W\}\) is given by  
\[d_{\zeta}(\sigma)=\Bigg{(}\frac{P_{T}\sqrt[10]{10^{C_{A}+\zeta_{U}}}}{\sqrt[10] {10^{N_{0}+\sigma}}10^{2 log_{10}\zeta-14,86+0.1L_{\zeta}}}\Bigg{)}^{\frac{1}{ 7}}, \tag{3}\]  
where \(P_{T}\) is the transmit power, \(G_{A}\) and \(G_{U}\) are the transmit and receive side gains, \(f_{c}\) is the carrier frequency, \(N_{0}\) is the thermal noise, and \(L_{\zeta}\) is the propagation loss when utilizing path \(\zeta\) that refers to LoS (hereafter denoted as \(L\)), side reflection (\(R\)), and through-windshield scenarios (\(W\)).  
The critical distance for under-vehicle propagation (\(U\)) can only be obtained numerically by utilizing the following expression:  
\[P_{T_{W}}-\sigma=20\text{log}_{10}d_{U}(\sigma)+20\text{log}_{10}f_{c}-147.55+ \phi d_{U}\sigma^{-\psi}, \tag{4}\]  
where \(P_{T_{W}}\) is the transmit power in watts, and \(\phi\) and \(\psi\) are the constants that characterize specifics of the antenna location [23]. Then, if we consider the critical sensitivity of transmitters \(\sigma_{\min}\), this enables us to derive the uttermost communication distance for single-hop communication as  
\[d_{\zeta}^{max}=\Bigg{(}\frac{P_{T}\sqrt[10]{10^{C_{A}+G_{U}}}}{\sqrt[10]{10^{ N_{0}+\sigma_{min}}}10^{2 log_{10}f_{c}-14.86+0.1L_{\zeta}}}\Bigg{)}^{\frac{1}{7}}, \tag{5}\]  
for path \(\zeta\in\{L,R,W\}\), and similarly by setting \(\sigma=\sigma_{min}\) in (4) for evaluating \(d_{U}^{max}\).  
In our model, the data rate is defined by the MCS specified in IEEE 802.15.3d standard. That is, once the communication distance falls into a range limited by received signal power, the corresponding data rate is shown in Table 2.
# An Improved Jacobi-Based Detector for Massive MIMO Systems
## 2 System Model
### Conventional Jacobi Algorithm  
To avoid the direct matrix inverse of \(\mathbf{A}\), we can convert the MMSE detection into solving the linear equation as  
\[\mathbf{A}\mathbf{\tilde{x}}=\mathbf{b}. \tag{3}\]  
By utilizing Jacobi algorithm [18], the matrix \(\mathbf{A}\) can be decomposed as \(\mathbf{A}=\mathbf{D}+\mathbf{E}\), where \(\mathbf{D}\) and \(\mathbf{E}\) are respectively considered as the diagonal and off-diagonal components parts of \(\mathbf{A}\). Then, the iterative solution of the Jacobi algorithm is given by  
\[\mathbf{\tilde{x}}^{(k)}=\mathbf{D}^{-1}\left[\left(\mathbf{D}-\mathbf{A} \right)\mathbf{\tilde{x}}^{(k-1)}+\mathbf{b}\right], \tag{4}\]  
where \(k\) denotes the iteration index. Since the convergence rate of the iteration is highly based on the initial setting, we consider the initial solution \(\mathbf{\tilde{x}}^{(0)}\) as \(\mathbf{\tilde{x}}^{(0)}=\left(\mathbf{D}^{-1}-\mathbf{D}^{-1}\mathbf{E} \mathbf{D}^{-1}\right)\mathbf{b}\) in this work [23].  
Figure 1: The structure of multiple-input-multiple-output (MIMO) signal detection.
# A Sibelobe Suppressing Beamformer for Coherent Plane Wave Compounding
## 4 Discussion  
The results of CPWC and CPWC + SVD applied to the simulated and the phantom data have been presented. From the results, we can see that the proposed method is useful in achieving a better performance than the traditional compounding method in terms of the sidelobe suppression and contrast enhancement. Compared to the traditional sidelobe suppression methods, our approach has two main advantages:  
First, the proposed coherence-based method does not affect the lateral resolution as the apodization methods do. Our CPWC + SVD method is conducted on the receiver end after the DAS without manipulation of the waveform. Then, the coherence filter only removes the sidelobe and the noise among the CPWC frames, adaptively. Thus, the mainlobe is preserved during our process. Note that we chose 1 as the number of eigenvalues or eigenvectors in the adaptive filtering. This is due to our wish to reduce the sidelobe as much as possible.  
Second, the contrast enhancement of the adaptive beamforming methods, such as the MV beamformer, comes at the cost of an extra computational load, which is too heavy for the plane wave imaging and CPWC. Suppose \(l\) is the subarray length, the MV has the complexity O(\(l^{3}\)) by using Gaussian elimination for one covariance matrix inversion [28]. For each angle, the matrix inversion process should repeat (\(L\)-\(l\)) times for all subarrays. For \(n=11\) and \(l=32\), the MV requires \(\frac{2}{3}\times 32^{3}\times(128-32)\times 11=23068672\) floating operations in addition. However, the additional computation of our algorithm is relatively acceptable. As we know, the main computational amount occurs on the SVD of the covariance matrix. Since the matrix is a square matrix, the SVD equals the eigen-decomposition. The eigen-decomposition of the covariance matrix requires \(21n^{3}\) floating operations by using the Golub-Reinsch algorithm [29]. In other words, the computational amount is proportional to the cube of the angle number. Taking our experiments with 11 angles as an example, it will only require \(21\times 11^{3}=27951\) floating operations in addition. The lack of an additional artifact is also an advantage of CPWC + SVD over the MV.  
Aside from PWI, our proposed beamformer can also be used in the synthetic aperture (SA) imaging method since the SA is conceptually similar to CPWC [30; 31]. In the SA method, RF data are obtained from pulse-echo sensing events of multiple point source firings at different lateral positions which are similar to the firings from different angles in CPWC. Broadly speaking, CPWC + SVD may be combined with other ultrasound imaging modes with several different firings.
## 1 Introduction  
Anomalies can be identified as abnormal points that have different characteristics from the majority of the data, and such novel observations may result from a specific failure, unexpected noise, etc., in industrial facilities monitoring. Online anomaly detection of industrial data streams can support operation and maintenance staff in identifying and locating potential equipment failures in a timely and accurate manner, avoiding serious faults and accidents.  
Today, a complex data stream is common from Distributed Control System (DCS) and IoT devices in many industry fields, where multivariate data of infrastructures with complex correlations and time-varying characteristics are continuously arriving. The complexity brings an insurmountable challenge to anomaly detection, and the evolving data streams may make the preprocessing or trained models quickly outdated.  
This paper concerns the computational method to deal with complex evolving data streams. Incremental learning-based anomaly detection provides an effective way to address the challenge. In particular, a competitive neural network has been widely used, as it is appropriate for an unsupervised setting that is natural for anomaly detection with rare labels. Existing state-of-the-art competitive neural network models, such as Growing Neural Gas (GNG) [1, 2], Self-Organizing Neural Network (SONN) [3, 4], Adaptive Resonance Theory (ART) [5], however, are designed for static offline data and cannot effectively cope with evolving data streams. There are a few competitive neural network-based methods proposed for time series anomaly detection [6; 7; 8]. However, they focus on learning the time-varying characteristic via adding adaptive learning strategies, ignoring the evaluation of the computational overhead of improved algorithms for evolving data streams.  
The goal of this paper, thus, is to provide a novel framework for online anomaly detection that adaptively adopts the baseline GNG algorithm in an online setting, effectively dealing with the challenges of complex and evolving data streams.
## 2 Methodology
### Methods for Evaluating Data Quality and Selecting Data  
Since the sampling frequency of the quantum sensor was higher than that of the spectrometer, multiple _PAR_ data could always be collected during the measurements of \(E\) and \(L\). We calculated the coefficients of variation of incident _PAR_, including \(CV_{E}\), \(CV_{L}\), and \(CV_{section}\), to evaluate illumination variations during the measurement intervals. \(CV_{E}\), \(CV_{L}\), and \(CV_{section}\) represent the illumination variations during the irradiance measurement, the radiance measurement, and the measurement section, respectively (Figure 3). They were calculated as follows:  
\[CV_{E}=\frac{\sigma_{(PAR_{E})}}{\mu_{(PAR_{E})}} \tag{4}\]  
where \(\sigma_{(PAR_{E})}\) represents the standard deviation and \(\mu_{(PAR_{E})}\) represents the mean _PAR_ recorded during the downwelling irradiance measurement.  
\[CV_{L}=\frac{\sigma_{(PAR_{L})}}{\mu_{(PAR_{L})}} \tag{5}\]  
where \(\sigma_{(PAR_{L})}\) represents the standard deviation and \(\mu_{(PAR_{L})}\) represents the mean _PAR_ recorded during the upwelling irradiance measurement.  
\[CV_{section}=\frac{\sigma_{(PAR_{section})}}{\mu_{(PAR_{section})}} \tag{6}\]  
where \(\sigma_{(PAR_{section})}\) represents the standard deviation and \(\mu_{(PAR_{section})}\) represents the mean _PAR_ recorded during the measurement section.  
Figure 3: The calculations of \(CV_{E}\), \(CV_{L}\), and \(CV_{section}\). The bottom axis represents time. The red cycles represent the high-frequency _PAR_ measurements of the ML-020P quantum sensor. The blue rectangles represent the spectral measurements of the spectrometer, including the measurements of radiance and irradiance.  
Using the three \(CV\)s, the following rules were applied to filter out low-quality SIF data retrieved by the SFM and 3 FLD.  
\[CV_{E}<a\ \&\ CV_{L}<b\ \&\ CV_{section}<c \tag{7}\]  
where \(a\), \(b\), and \(c\) are the thresholds. Since the SVD method computes the SIF value solely based on the \(L\) measurement, only illumination stability during \(L\) measurement should be considered. Therefore, we used a rule based on \(CV_{L}\) to remove low-quality SIF data retrieved by SVD.  
\[CV_{L}<b \tag{8}\]  
The number of quantum sensor measurements used for the calculation of the three \(CV\)s depends on the length of the spectral measurement period. In this experiment, the average number of quantum sensor measurements used to calculate \(CV_{E}\), \(CV_{L}\), and \(CV_{section}\) were about 4, 6, and 48, respectively.  
As mentioned in Section 2.1.2, the relationship between the retrieved SIF and PAR was used to evaluate the performance of the above data filtering methods.
## 2 Materials and Methods
### Evaluation  
The procedure used to train and evaluate model candidates during and after the optimization process was to use a cross-validation scheme adopting training and testing sets for each iteration. Additionally, a fraction of the training set is used to assess whether the model is learning. The variation of the training and validation sets may impact on the capacity to evaluate the model robustly. Therefore, this approach provides more stable results. A two-fold cross-validation (TFCV) scheme was used during the optimization procedure, as a longer validation process would have been considerably time-consuming, and a five-fold cross-validation (FFCV) scheme was used to retrieve the performance of the final optimized models. On each fold iteration of the TFCV, 50% of the data are used for training, 30% for testing, and 20% for validation. On the other hand, during FFCV, the ratios are 60%, 20%, and 20%, respectively.  
The performance metrics adopted were the accuracy, recall, precision, and area under the receiver operating characteristic curve (ROC-AUC), computed using a 'one vs. rest' (OVR) approach. To avoid any dependency on the distribution of the training set, the metrics were computed as the macro average across the three classes. The metrics are expressed as a function of the terms of the confusion matrices of each class. For a single model, the performance of all folds is averaged. Additionally, the F1-score is another  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Labels** & **1 *** & **1.5 *** & **2 *** & **2.5 *** & **3 *** & **3.5 *** & **4 *** & **4.5 *** & **5 *** \\ \hline Star rating (\%) & 4.3 & 0.7 & 3.5 & 2.0 & 10.6 & 10.2 & 29.7 & 12.5 & 26.6 \\ \hline Polarity (\%) & & 8.47 & & & 22.76 & & & 68.77 & \\ \hline Imbalance ratio & & 1:8 & & 1:3 & & & 1:1 & \\ \hline Class & Negative & & Neutral & & & Positive & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ratings distribution and corresponding sentiment polarity label and imbalance ratio, with corresponding classes.  
Figure 1: Top 20 most frequent tokens of Zomato Portugal dataset between 1 April 2014 and 2 September 2022: (**a**) frequency of all tokens; (**b**) frequency of all tokens after removing the stopwords; (**c**) frequency of all tokens after removing the stopwords and punctuation marks.  
relevant metric in the literature, which is computed as the harmonic average between precision and recall.
# An Innovative System of Deep In Situ Environment Reconstruction and Core Transfer
## 4 Temperature and Pressure Decoupling Control Based on the Iso-Density Curves of Water
### Digitization of the Iso-Density Curves of Water  
Equation (1) is too complicated to use in the temperature and pressure control. Alternatively, we create a set of iso-density P-T curves on the surface in Figure 3, and project these curves to the P-T plane; then, we get the iso-density P-T curve cluster of water as shown in Figure 4. Under the condition of constant density, the P-T relationship of water varies along a certain curve in the cluster. When the temperature changes, the pressure will change accordingly. When filling or draining water, the P-T relationship will jump to another curve due to the change of the water density. The mutual influence between temperature and pressure of water in the chambers makes it difficult to control the temperature and pressure precisely. To this end, we propose a P-T interpolation control method based on the iso-density P-T curves of water, which decomposes a P-T coupling path into two kinds of independent interpolation paths: iso-thermal pressure control and iso-density temperature control. The former only needs pressure control, and the latter only needs temperature control. As shown in Figure 4, the straight line \(\overline{\text{AF}}\) is a P-T coupling path, and it can be decomposed into two iso-density temperature paths (solid curves) AB and DE, and two iso-thermal pressure paths \(\overline{\text{BD}}\) and \(\overline{\text{EF}}\).  
Figure 3: State surface of water.  
Sample each iso-density curve to generate its fitting equation, and the iso-density P-T curve cluster \(S(\text{C})\) can be expressed as:  
\[S(\text{C})=\{Curve_{i}\ |\ \ i=1,\ldots,n\} \tag{2}\]  
where  
\[Curve_{i}\text{: }P_{i}=\)[\(f_{ij}(T)\ |\ T\in[T_{j},T_{j+1}]\)] (3)  
Is the \(i\)th iso-density curve equation, and \(P_{i}\) is a piecewise fitting function, with the \(T\) axis being divided into \(n_{i}\) segments by \(n_{i}+1\) points \(T_{s}=\) [\(T_{1}\),..., \(T_{ni+1}\)]. It can be seen in Figure 4 that the shapes of the curves are regular, with no large curvature change or inflection point. Therefore, we use cubic polynomials to perform piecewise curve fitting, with the fitting errors less than 0.1 bar. In this way, each curve \(Curve_{i}\) can be expressed as:  
\[P_{i}=\sum_{j=1}^{n_{i}}\sum_{k=0}^{3}a_{jk}T^{k} \tag{4}\]  
In which if \(T\
otin(T_{j},T_{j+1}]\), \(a_{jk}=0\ |\ k=0,\ 1,\ 2,\ 3\).  
If a state point \(S(P_{s},\ T_{s})\) satisfies Equation (5), then it is on the curve \(Curve_{s}\).  
\[|P_{s}-P_{i}|\leq\delta_{c} \tag{5}\]  
where \(\delta_{c}\) is the error threshold.
# Knowledge-Driven Network for Object Detection
## 4 Experiments
### The Evaluation on Different Categories  
First, we analyzed the results on _Pedestrian_, _Rider_, and _Motorbike_. Figure 5 shows the results of a typical scene that were obtained using four methods. As shown in Figure 5, the object in the image is ambiguous and there is occlusion; it is very challenging to extract the object region. Figure 5a-c shows the detection results of YOLOv3 [33], RetinaNet [34], and Faster-RCNN [8], respectively. Note that YOLOv3 [33] and RetinaNet [34] failed to distinguish between the pedestrian and the rider. As for the RPN-based method, the number of proposals was relatively large; thus, Faster R-CNN [8] can effectively detect objects, while the byproduct is a large number of false-positive detections. In contrast, our proposed method in this paper combined the geometric features and corner features between objects and successfully detected objects while using knowledge-driven methods to effectively avoid false detection.  
The second set of experiments was _Bike_ and _Rider_, shown in Figure 6. It can be seen that this task was challenging, because the objects to be detected were very close. In this complicated background, YOLOv3 [33] missed the objects, and the Faster-RCNN [8] and RetinaNet [34] algorithms not only failed to detect the right objects, but also generated a large number of false bounding boxes. However, KDNet (Figure 6d) combined the information from features that can help the detect the object more accurately. This shows that adding the category information in the geometric feature can increase the semantic information between the objects. After the corner feature and the geometric feature were merged, it effectively avoided false detection of missing parts.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **mAP** & _Rider_ & _Motorbike_ & _Bike_ & _Pedestrian_ & _Helmet_ \\ \hline YOLOv3 & 0.424 & 0.392 & 0.510 & 0.414 & 0.449 & 0.353 \\ RetinaNet & 0.683 & 0.665 & 0.733 & **0.747** & 0.625 & **0.643** \\ Faster R-CNN & 0.560 & 0.510 & 0.652 & 0.652 & 0.560 & 0.427 \\ KDNet (Ours) & **0.709** & **0.718** & **0.783** & 0.737 & **0.703** & 0.603 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The mAP and AP of the tested algorithms.  
Figure 5: The detection results of the position of _Pedestrian_, _Rider_, and _Motorbike_.  
Figure 6: The detection results of the position of _Rider_ and _Bike_.