esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling and Assessment of GPS/BDS Combined Precise Point Positioning
## 4 Parameter Differences between Combined PPP Models
### Station Clock and Ambiguity  
In the simplified GPS/BDS PPP model, station clock, and ambiguity estimates absorb the ISB parameters together, and the percentage which goes into each parameter depends on the normal equation. In Equation (9), the ISB assimilation shows the opposite sign for station clock and GPS  
Figure 6: Magnitude distribution of all position differences of GPS/BDS combined static PPP between the traditional and simplified models in the North, East, and Up components. All subplots exhibit normal distributions. The top-left corner of each subplot shows the bias and the standard deviation (\(\sigma\)), as well as the percentages of deviations that are within \(2\sigma\) and \(3\sigma\).  
Figure 7: Coordinate Difference between traditional and new model of JFNG at DOY 028, 2014. (a) Coordinate difference of the first 100 epochs; (b) Coordinate difference after 100 epochs.  
ambiguity parameters, thus the sum quantity of station clock and GPS ambiguity for the traditional and simplified model is theoretically the same at each epoch. The upper subplot of Figure 8 shows the magnitude distribution of this sum quantity differences between the two models using all GPS satellite/station pairs at all epochs. The mean difference is 2.0 \(\upmu\)m and all the differences are below 1 mm with around 0.35% bigger than 0.1 mm.  
According to Equation (9), the epoch-wise sum of station clock and BDS ambiguity in the simplified model is theoretically the same as the sum quantity of station clock, ISB, and BDS ambiguity in the traditional model. The bottom subplot of Figure 8 shows the magnitude distribution of this sum quantity differences between the two models using all BDS satellite/station pairs at all epochs. The mean difference is 4.6 \(\upmu\)m and all the differences are below 1 mm at around 0.72% bigger than 0.1 mm. Both plots show that the differences are so small, which further proves the same coordinate estimates in these two models.
## 5 Experiment Design and Results
### Data Sets Description  
This study uses four data sets to validate the proposed forecasting method. These four data sets are obtained from four different regions of Turkey (Cesme-Izmir, Amasra-Bartin and Pirbasi-Kayseri, and Gokceada-Canakkale). The selected locations are located in different parts of Turkey with different climates and wind regimes. The selected locations are shown in Figure 5.  
The four data sets are hourly data points from 1 January 2001 to 31 March 2021. Each data set contains hourly wind speed, surface pressure, wind direction, humidity, and temperature values. The data is divided into two subgroups training and testing. The training and testing data contain 80% and 20% of the whole data set, respectively. In order to show wind speed characteristics of the selected locations, Figure 6 depicts the daily averages of
## 5 Numerical Examples
### Stationary Wind Fields  
In terms of stationary wind field, the widely used two-sided Kaimal spectrum is selected as the target PSD, i.e.,  
\[S(\omega)=\frac{200}{4\pi}\frac{z}{U}\frac{u_{*}^{2}}{\left[1+50\frac{\omega z}{2 \pi U}\right]^{\frac{p}{3}}} \tag{34}\]  
where the height of simulation points \(z=60\) m; the mean wind velocity \(U=40\) m/s; the shear velocity \(u_{*}=kU/\ln(z/z_{0})\) in which the coefficient \(k=0.4\) and the ground roughness \(z_{0}=0.01\) m. It is assumed that all the simulation points possess the same auto-PSDs.  
The coherence function between two simulation points adopts the following Davenport's coherence function with the complex phase [37]  
\[\gamma_{jk}(\omega)=\exp\left(-\frac{C_{y}|y_{j}-y_{k}|\omega}{2\pi U}\right) \exp\left(-i\frac{(y_{j}-y_{k})\omega}{v_{app}}\right) \tag{35}\]  
where \(y_{j}\) and \(y_{k}\) = the horizontal coordinates of Points \(j\) and \(k\); \(C_{y}\) = attenuation coefficient and takes 10; \(v_{app}\) = apparent wave velocity and takes 10 m/s. Then, the deterministic phase \(\theta_{jk}(\omega)\) in simulation formulation can be readily calculated by the aforementioned two schemes, based on the given coherence function. The typical phase results \(\theta_{42}(\omega)\) obtained by the classical formula and separated phase are, respectively, shown in Figure 2. It can be seen that the two phases are periodic and the period of the latter is twice that of the former. Additionally, the value range of the latter is twice that of the former.
## 2 Kinematic Modeling  
### General Structure of CDPRs  
Most CDPRs have a similar composition, mainly consisting of a base body, a moving platform, and a number of driving cables. The schematics of general CDPRs are shown in Figure 1. There are \(m\) attachment points \(\mathrm{B}_{i}(i=1,...,m)\) on the fixed base, \(m\) end points \(\mathrm{A}_{i}(i=1,...,m)\) located at the moving platform, and \(m\) driving cables connecting the moving platform and the fixed base. The moving platform could change its positions and orientations by changing the cable lengths. According to the arrangements of the cables, two kinds of CDPRs could be considered: suspended CDPRs where all the driving cables are above the end-effector and non-suspended CDPRs where at least one driving cable is below the end-effector. In addition, according to the relationship between the driving cable number \(m\) and the controllable DOF of the moving platform \(n\), CDPRs could be classified into two groups, i.e., redundantly--(\(m>n\)) and non-redundantly--(\(m<=n\)) actuated CDPRs. In this paper, the proposed automatic calibration method aims at redundantly-actuated CDPRs (i.e., \(m>n\)) and is applicable for both suspended and non-suspended CDPRs.
## 3 Results and Discussion  
### Evaluation of Deep Learning Models  
The performance of the five models across four evaluation metrics (accuracy, precision, recall, and F1-score) for the multiclass classification task is summarized in Table 3.  
The accuracy of the training and test sets for all models steadily increased throughout training. As the number of epochs increased, the training accuracy improved consistently, and the test set accuracy stabilized after reaching a certain value, indicating that none of the five models experienced overfitting. VGG16 exhibited good performance with minimal  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **AlexNet** & **GoogLeNet** & **VGG16** & **ResNet50** & **ViT** \\ \hline Accuracy & 0.969 & 0.973 & 0.993 & 0.984 & 1 \\ Precision & 0.970 & 0.973 & 0.993 & 0.984 & 1 \\ Recall & 0.969 & 0.973 & 0.993 & 0.984 & 1 \\ F1-score & 0.970 & 0.973 & 0.993 & 0.984 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance of the five models across four evaluation metrics.  
variation compared to the other models. Although AlexNet showed the lowest accuracy, it still achieved a score close to 1.0 on the test set after 30 training epochs (Figure 5).  
Figure 5: Accuracy and loss of five evaluated models (the X axis: training epochs; the Y axis: score).
## 4 Results and Analysis
### Experiment Setup  
In order to evaluate the performance of the proposed OCC system with the grid virtual division scheme, we build four prototype systems and demonstrate its experimental setup, as shown in Figure 6. At the transmitter side, we use two types of LED luminaires with different sizes and shapes (round LEDs with diameters of 0.13 m and 0.3 m, square LEDs with a side length of 0.14 m \(\times\) 0.14 m and 0.25 m \(\times\) 0.25 m) as transmitters in our proposed system. Specifically, we build the LED driver with commercial off-the-shelf components, which converts alternating current (AC) into direct current (DC) for further modulation via a low-cost MOSFET of SI2310A to drive the selected LED luminaires. An ARM Cortex-M4 GD32F330G8U6 microcontroller controls the MOSFET to generate modulation symbols, as shown in Figure 6a. The modulation method utilizes the ON-OFF Keying (OOK), thanks to its simplicity, without losing its generality. In addition, in order to make the data transmission more reliable, the original data are split into \"packets\" for batch transmission. At the receiver side, we take the CMOS camera of the iPhone 8 plus with 12-megapixels as the receiver and configure the smartphone camera to work in the preview mode with a frame rate of 30 fps. The exposure time of the camera is set to 1/8000 s, and the sensitivity (ISO) of the camera is set to 350. The detailed parameters are shown in Table 1. Last but not least, we demodulate and decode the optical communication signal through the built-in iOS application of the iPhone 8 Plus (debugged with Xcode 12.4) to complete the data communication between the OCC transmitter and the OCC receiver.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Round transmitter No. 1 & 0.13 m \\ Round transmitter No. 2 & 0.3 m \\ Square transmitter No. 1 & 0.14 m \(\times\) 0.14 m \\ Square transmitter No. 2 & 0.25 m \(\times\) 0.25 m \\ Microcontroller & ARM Cortex-M4 GD32F330G8U6 \\ Modulation & On-Off Keying \\ Receiver & iPhone 8 Plus \\ Frame rate & 30 fps \\ Exposure times & 1/8000 s \\ ISO & 350 \\ Screen resolution & \(1920\times 1080\) \\ Pixel & dual 12-megapixel \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experiment parameters.
## 3 Results and Discussion
### Volatile Profiling Using HS-SPME/GC-MS
Total alcohol content exhibited the highest concentration in JLpAp (31.37%), featuring 11 alcohol compounds, whereas JCON, JLcAp, and JLhAp, featured 10 alcohol compounds at 333.20 ng/100 g (54.53%), 177.43 ng/100 g, and 176.61 (29.44%) respectively. Among bicultured samples, six alcohols (1-hexanol, 2-ethyl-hexanol, 2,3-butanediol, 2,4-di-tert-butylphenol, benzyl alcohol, phenylethyl alcohol) were consistently identified. The data suggested that 2,3-butanediol was pivotal as the primary alcohol in Junzao juibue pures, constituting nearly 43% of the total volatile concentration in the unrefmented pure. However, this concentration exhibited a decline after fermentation. Conversely, 1-decanol, 1-nonanol, and 1-octanol, present in JCON, were conspicuously absent in all bicultured pures. This decline may be attributed to the possible esterification of these alcohols with available acids in the unrefmented sample, contributing to the production of highly aromatic fermented products. Concurrently, certain volatiles--such as 1,2,3,4-tetrahydro-1,1,6-trimethyl (naphthalene)--experienced a reduction and remained undetected in the fermented samples.
# All About Audio Equalization: Solutions and Frontiers
## 4 Graphic Equalizers
### Cascade Graphic Equalizers
When the gain and Q value of the opposite filters are set appropriately, the ripple seen in the overall response can be reduced further. [PERSON] _et al._ refined this method by allowing the Q value of the band filters to change with gain [64]. [PERSON] _et al._ developed an accurate graphic equalizing method by constructing each band filter using several cascaded second order filters so that its magnitude response approximates a cosine pulse, spanning one band only [65]. Such high order filters have reduced interference, while the overall computational cost of the graphic equalizer is increased. [PERSON] _et al._ have proposed an optimization method, which needs good initial values and definition of the desired gain at command points as well as at additional intermediate frequency points and which then iteratively adjust the coefficients of the cascaded biquad filters to reduce the mean squared error [66]. [PERSON] and [PERSON] showed how cascaded high order equalizing filters can be effectively used in graphic and parametric equalizer design [52]. Each high order band filter has a steep roll-off at its crossover frequencies, so the interference between bands gets reduced in comparison to an equalizer based on second order band filters. Even a graphic equalizer based on fourth order band filters is considerably more accurate than the one based on second order filters, as the examples shown in [56] demonstrate. [PERSON] and [PERSON] presented a filter optimization algorithm for the high order graphic equalizer, which minimized errors in the transition bands by iteratively optimizing the orders of adjacent band filters [53].
## 1 Introduction
### Our Contribution  
From the above discussion, we have addressed two major issues (_i.e._, off-grid problem and efficient algorithms for real-time implementation) of applying CS to sparse continuous signal reconstruction. In this paper, motivated by the idea of H\(l\)1-DCD [40] and grid refinement approach [27], we present three fast and accurate sparse reconstruction algorithms (_i.e._, HR-DCD, Hlog-DCD and H\(l_{p}\)-DCD) which are based on homotopy, dichotomous coordinate descent (DCD) iterations and non-convex regularization, combining with the grid refinement technique to deal with the aforementioned issues. The main contributions of this paper are as follows: (1) we formulate the sparse recovery problem by homotopy DCD method with three typical classes of non-convex penalties, which are proved to recover sparsity in a more efficient way than homotopy DCD method with convex penalties as shown in [PERSON]'s previous researches [28,37,38,39,40]; (2) further, the grid refinement technique [27] is utilized to combine with our algorithms to alleviate the effect of off-grid problem and reduce the complexity simultaneously; (3) experimental results of three representative applications (DOA, passive radar imaging, ISAR imaging) are carried out to verify the effectiveness of the proposed methods.  
The outline of this paper is as follows: in Section 2, two sparse recovery problems are discussed. Section 3 describes the proposed methods and related analysis. In Section 4, extensive experimental results are presented to verify our methods. Finally, Section 5 draws the conclusions.  
Throughout this paper we shall make use of the following notation: bold-case letters are reserved for vectors and matrices, e.g., **x** is a vector, **A** is a matrix; Elements of **A** and **x** are represented as A\({}_{n,p}\) and x\({}_{n}\), respectively. \(I\) and \(\digamma\) denote the support and its complement, respectively. Further, we represent by \(\textbf{R}^{(q)}\) the \(q\)-th column of **R**; **A\({}_{I}\)** a matrix obtained from **A** keeping only columns corresponding to \(I\); \(\textbf{x}_{I}\) the subset of **x** that contains entries from **x** corresponding to \(I\); \(\left\|\textbf{x}\right\|_{p}\) denotes the \(l_{p}\) norm of a vector, \(\left\|\left\|\textbf{A}\right\|\right\|_{2}\) is the spectral norm of the matrix \(\textbf{A}\); \(\left\langle{}_{\textbf{}}\right\rangle\) denotes the inner product; \(\left(\cdot\right)^{H}\), \(\left(\cdot\right)^{*}\), Re\(\left\{\cdot\right\}\) denote the conjugate transpose, conjugate and real part operations, respectively.
# Invariant Feature Encoding for Contact Handprints Using Delaunay Triangulated Graph
## 3 Proposed Method
### Delaunay-Triangle-Graph (DTG) Algorithm  
The algorithm namely, the Delaunay triangulation graph (DTG) is introduced for handprints using minutiae or minutiae-like key points, and it focuses on an automated alignment of geometrically transformed images. Minutia-based triangles are generated from Delaunay triangulation. Now, this preliminary process estimates the possible set of matched triangles (likelihood) using a simple minutiae-based feature set as shown in Figure 3. By observing the figure, these sets of matched triangles cannot be the final matched triangles. However, existing past work considered these triangle sets as final matched triangles.  
Therefore, they can be checked further in the subsequent step by accepting or discarding the triangles using a validation mechanism because, in latent and contact-based hand prints, the triangles matched in the initial step may not be reliable due to the dispersion of minutiae. Therefore, this process requires further extension. The algorithm consists of following phases: (i) base triangle generation; (ii) extension of sub-graph; (iii) elimination of sub-graph; and (iv) merging of sub-graphs to form the DTG. The overall process of the DTG algorithm is illustrated in Figure 4.  
Figure 4: Overall flow of the Delaunay triangulated graph (DTG) algorithm.  
Figure 3: Phase 1: Initial output of the matched triangles in test and template images. They are called base triangles and act as reference triangles for further processing. Note that different colors indicate matched triangle pairs. One triangle can match more triangles at a time, and they do not show exact matches (i.e., false matches are possible here). This process shows only the possible matched triangles and provides landmarks for the next phase of the algorithm.
## 4 Discussion  
This paper proposes the development of a measuring step length method using a single LiDAR on healthy individuals. The aim is to validate the use of the single LiDAR to estimate accurately the heel cloud point using a polynomial method during both normal walk and fast walk. Figure 8 shows the cloud points data along the projected walkway. Despite the difficulty in obtaining a clearer U shape of the heel as the walking speed increases, by utilizing mathematical algorithms such as the polynomial curve fitting, as shown in Figure 9, further accuracy of the location of the cloud points can be achieved. Detailed summarization of the performance is available in Table 2.  
From Table 2, the measurements of the step length and standard deviation for normal walk and fast walk for all subjects are presented for both the visual inspection method and polynomial method. From the results, the average error in step length was found as \(\pm 0.0515\) and \(\pm 0.0625\) for normal walk and fast, respectively, while that of the visual inspection method reported \(\pm 0.0633\) and \(\pm 0.0874\) for normal walk and fast, respectively. This translates to a 5-9 cm error in step length. From this, the performance of the proposed method is comparable to the visual inspection with an acceptable error. With the accuracy difference estimated to be at most 2 cm, the polynomial method provides reliability of heel location estimation as compared with the observational gait analysis. Moreover, polynomial method offers automated analysis when it comes to huge data as opposed to visual inspection by clinicians. This is because, for visual inspection analysis done by clinicians, there might exist inconsistencies or disagreements in the descriptive terminology between them [47]. There was a significant difference between the two methods, with a \(p\)-value of 0.5053. The interquartile range (IQR) of the visualization inspection method and polynomial fitting method for normal speed walk were 0.0541 and 0.0564, respectively. For faster speed walk, the interquartile range of the visualization inspection and polynomial fitting method were 0.1155 and 0.1533, respectively. From the reported IQR, the polynomial estimation method performs on par with the expert inspection.
###### Abstract  
This study aimed to examine the acute effects of squat and ballistic jump exercises during warm-ups on judo-specific performance in young male judokas. Using a randomized crossover design, 10 sub-junior male judokas (age: 12.9 \(\pm\) 0.7 years) completed three conditions: a controlled warm-up with only judo-specific exercises and two experimental warm-ups including either a three-repetition maximum (RM) back squat (with ~90% 1 RM load) or ballistic jumps (squat jumps, scissor jumps, and double-leg bounds) in addition to judo-specific warm-ups. Following each warm-up condition, participants performed the Special Judo Fitness Test (SJFT), with heart rate measured immediately and one minute post-test. Handgrip strength and ratings of perceived exertion (RPE) were recorded after the SJFT. Both squat and ballistic jump exercises significantly improved judo-specific performance compared to the control condition, with large effect sizes (ESs). The number of throws in set 2 (\(p\) = 0.001, \(\eta_{\text{p}}\)\({}^{2}\) = 0.65, large ES), total throws (\(p\) < 0.001, \(\eta_{\text{p}}\)\({}^{2}\) = 0.70, large ES), and the SJFT index (\(p\) < 0.001, \(\eta_{\text{p}}\)\({}^{2}\)= 0.65, large ES) all showed significant improvements. Regarding the throw in set 2, significant improvements were observed after both squat (\(p\) = 0.003, Hedge's g = 1.78, large ES) and ballistic jump exercises (\(p\) = 0.010, Hedge's g = 1.44, large ES) compared to the control condition. Similarly, total throws were significantly higher in the squat (\(p\) = 0.003, Hedge's g = 1.51, large ES) and ballistic jump (\(p\) < 0.001, Hedge's g = 1.37, large ES) conditions compared to the control condition. Furthermore, the SJFT index showed notable improvements following squat (\(p\) = 0.010, Hedge's g = 0.80, moderate ES) and ballistic jump (\(p\) < 0.001, Hedge's g = 0.90, moderate ES) conditions compared to control conditions. However, squat exercises led to a significant reduction in right-hand grip strength (\(p\) < 0.001, \(\eta_{\text{p}}\)\({}^{2}\) = 0.58, large ES) [\(p\) = 0.008, Hedge's g = 0.19, trivial ES for squat vs. control; \(p\) = 0.014, Hedge's g = 0.23, small ES for squat vs. ballistic jump], with no differences observed in left-hand grip strength or RPE scores (\(p\) > 0.05). In conclusion, the integration of squat and ballistic jump exercises into warm-up protocols has been shown to significantly improve judo-specific performance in young male judokas without eliciting an increase in RPE values. However, careful consideration should be given when incorporating squat exercises, as they may lead to localized handgrip fatigue (reduced grip strength due to muscle exhaustion), which could affect performance in grip-dependent techniques.  
p 20241410558[[https://doi.org/10.3390/appl42210558](https://doi.org/10.3390/appl42210558) [https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://doi.org/10.3390/appl42210558](https://doi.org/10.3390/appl42210558) [https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))
# Load Forecasting Based on LVMD-DBFCM Load Curve Clustering and the CNN-IVIA-BLSTM Model
## 3 Methodology
### The Proposed CNN-IVIA-BLSTM Forecasting Model  
Using the CNN-IVIA-BLSTM model to forecast the power load data:  
Step 1. Feature fusion extraction. CNN is used to extract the data feature.  
Step 2. Forecasting. IVIA optimizes the parameters related to the BLSTM network, then uses the optimized BLSTM to predict.  
Step 3. Power load forecast evaluation. The load data forecasting effect analyzed using RMSE, MAE, and MAPE.