esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Human activity recognition (HAR) has been an active area in computer vision with a broad range of applications, such as education, security surveillance, and healthcare. HAR is a general time series classification problem. LSTMs are widely used for time series classification tasks. However, they work well with high-dimensional feature vectors, which reduce the processing speed of LSTM in real-time applications. Therefore, dimension reduction is required to create low-dimensional feature space. As it is experimented in previous study, LSTM with dimension reduction yielded the worst performance among other classifiers, which are not deep learning methods. Therefore, in this paper, a novel scale and rotation invariant human activity recognition system, which can also work in low dimensional feature space is presented. For this purpose, Kinect depth sensor is employed to obtain skeleton joints. Since angles are used, proposed system is already scale invariant. In order to provide rotation invariance, body relative direction in egocentric coordinates is calculated. The 3D vector between right hip and left hip is used to get the horizontal axis and its cross product with the vertical axis of global coordinate system assumed to be the depth axis of the proposed local coordinate system. Instead of using 3D joint angles, 8 number of limbs and their corresponding 3D angles with \(X\), \(Y\), and \(Z\) axes of the proposed coordinate system are compressed with several dimension reduction methods such as averaging filter, Haar wavelet transform (HWT), and discrete cosine transform (DCT) and employed as the feature vector. Finally, extracted features are trained and tested with LSTM (long short-term memory) network, which is an artificial recurrent neural network (RNN) architecture. Experimental and benchmarking results indicate that proposed framework boosts the performance of LSTM by approximately 30% accuracy in low-dimensional feature space.  
human activity recognition (HAR); Kinect depth sensor; 3D posture data; egocentric coordinate system; dimension reduction; discrete cosine transform (DCT); deep learning; LSTM
# A Noncontact Method for Calibrating the Angle and Position of the Composite Module Array
## 3 Calibration Method
### Influence of Array Placement Error on Calibration
#### 3.2.1 Influence of Array Placement Error on Angle Calibration
Therefore, in order to ensure that the 2-D relative angle between the composite module does not exceed 300\({}^{\prime\prime}\), it is proposed that each module be supported by three fine threads and fixed by one ordinary thread. The design of the composite module array base is shown in Figure 7. By tightening or loosening the fine threads, the deflection angle of the composite module in the corresponding direction is adjusted.
## 3 Experimental Verification  
To further verify the reliability of the two-phase coupling model, an experiment on the proppant transport in visualized fractures was conducted. The experimental device consisted of six main parts (Figure 2). The experimental steps were as follows:  
* Prepare the proper amount of proppant and fracturing fluid with different viscosities.
* The proppant and the fracturing fluid are thoroughly mixed by a mixing system.
* The mixed slurry is pumped to the visual fracture system through the pumping device.
* Collect the proppant placement morphology in the fracture device at different times through the camera system.
* Waste treatment and recycling.  
Figure 2: Experimental device schematic. The device consists of six parts: the proppant and fracturing fluid, pumping system, flowmeter, visual fracture device, camera system, and recovery system.  
Through physical experiments and numerical simulations, the flow of proppant in the fracturing fluid (slip water) was studied. In order to truly reflect the flow behavior of proppant particles in fractures, it is necessary to ensure that the dynamic behavior of the slurry in the numerical simulation is similar to that in physics experiments. Therefore, based on the principle of equal linear speed, the oilfield pumping rate wasw converted into the experimental pumping rate and the inlet speed of the numerical simulation. The conversion formula is as follows:  
\[v_{1}=\frac{v_{2}}{h_{2}\times w_{2}\times 2}\times(h_{1}\times w_{1})\times 1000 \tag{9}\]  
where \(v_{1}\) is the experimental pumping rate, \(v_{2}\) is the oilfield pumping rate, \(h_{1}\) is the experimental fracture height, \(h_{2}\) is the oilfield fracture height, \(w_{1}\) is the experimental fracture width, and \(w_{2}\) is the oilfield fracture width.  
The experimental parameters are listed in Table 1.  
To verify the accuracy of this fluid-particle two-phase coupled flow model, the simulation results at different times were compared with the experimental results (Figure 3). Owing to the influence of perforation, the flow velocity of the fluid near the fracture inlet is high and changes rapidly, and the proppant hardly settles down.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Oilfield** & **Experimental** & **Simulation Inlet** & **Proppant** \\
**Pumping Rate** & **Pumping Rate** & **Speed** & **Concentration** \\
**(m\({}^{3}\)/min)** & **(L/min)** & **(m/s)** & **(\%)** \\ \hline
10 & 195 & 0.93 & 2 \\ \hline
**Proppant diameter** & **Proppant density** & **Fluid viscosity** & **Fluid density** \\
**(mm)** & **(kg/m\({}^{3}\))** & **(mPa-s)** & **(kg/m\({}^{3}\))** \\ \hline
1 & 1350 & 3 & 998 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental and numerical simulation parameters.  
Figure 3: Experimental and numerical simulation results at different times: **(a)** T = 20 s; **(b)** T = 60 s; **(c)** T = 100 s; **(d)** T = 180 s.
# Bit Independence Criterion Extended to Stream Ciphers
## 4 Experiments and Discussion of the Results
### Scenario 1 (BIC in Random SAC Matrices)
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{\((n,m)\)} & \multicolumn{4}{c}{\(I\)} \\ \cline{2-5}  & **4096** & **8192** & **16,384** & **32,768** \\ \hline (32, 32) & 0.906793 & 0.938818 & 1.06287 & 0.930434 \\ \hline (64, 64) & 1.04064 & 1.02569 & 0.97230 & 1.04773 \\ \hline (8, 64) & 0.972279 & 0.939652 & 1.05472 & 0.923853 \\ \hline (64, 32) & 0.994157 & 0.997373 & 1.06138 & 0.983795 \\ \hline \end{tabular}
\end{table} 
Table 5: Observed \(\widehat{\sigma^{2}}(Z_{T}|H_{0})\) values for each selected \(n,m,l\) value. Figure 2: Adjustment of the observed distribution from \(Z_{T}\) to \(N(0,1)\) in \(H^{i}\) random matrices, which satisfy the BIC. It is observed that in all cases, the \(p\)-values are greater than the usual values assumed for \(\alpha\), such as 0.01 or 0.05 and are consistent with the assumed normality hypothesis. The higher the value of \(n=m\), the higher the \(p\)-value, which corresponds to the influence of these parameters on the value of \(\lambda\) (see Table 3). _BIC test application on \(H^{i}\) random matrices_. To evaluate the behavior of the BIC test in random matrices, each \(Z_{t}\) was compared with the critical value \(Z_{1-\alpha_{2}}\), and the number of rejections of \(H_{0}\) was counted. Tables 7 and 8 show the results for various levels of significance \(\alpha_{2}\) and \(l=16\),384. The observed number of rejections is expected to correspond to that expected according to the selected \(\alpha_{2}\) level, which would allow choosing \(\alpha_{2}\), to obtain zero rejections in this scenario. For the value of \(\alpha_{2}=0.0001\) located in the last row of both tables, no statistical dependence is detected as expected in random matrices, confirming the effectiveness of the criterion and illustrating the importance of the proper selection of \(\alpha_{2}\), according to the number \(d=n\cdot C_{2}^{m}\) of pairs of columns whose independence is evaluated. For the values of \(l,n,m,\alpha_{1},\alpha_{2}\) used, such that no Type I error is made, the probability of making a Type II error must be calculated and the values that minimize it must be chosen. In this sense, experiments will be carried out in the second scenario on a stream cipher.
## 2 Results and Discussion
### Candidate Transcription Factors Involved in Regulating the Terpenoid Biosynthetic Pathway  
TFs play key roles in controlling gene expression [51], and the controlled transcription of biosynthetic genes is one major mechanism regulating secondary metabolite production in plant cells [52, 53, 54]. The floral terpenoids of snapdragon appear to be derived exclusively from the MEP pathway in plastids, and this pathway controls precursor levels for GPPS, which in turn is transcriptionally regulated [55]. In our _G. rigescens_ unigene dataset, 7176 unigenes were annotated as transcription factors (Table S6), including bHLH (349), AP2-EREBP (172), WRKY (141), MYB (129), bZIP (115), and GRAS (94) family members. Among these, most were expressed in both root and leaf tissues, with 80 showing a significantly higher expression level in leaves than in roots (Table 6, Table S7).  
Members of the WRKY transcription factor family have been shown to regulate secondary metabolism pathways [56]. In _Gossypium arboreum_, GaWRKY1 regulates sesquiterpene biosynthesis via activation of \(\delta\)-cadinene synthase (CAD1-A) [57]. In _Coptis japonica_, the biosynthesis of b  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**TF Family**} & **Number of** & **Up-Regulated in Leaves** & **Up-Regulated in Roots** \\  & **Genes Detected** & **(log\({}_{2}\)(Fold Change) \(>\) 2)** & **(log\({}_{2}\)(Fold Change) \(>\) 2)** \\ \hline HLH & 349 & 26 & 5 \\ AP2-EREBP & 172 & 20 & 4 \\ WRKY & 141 & 17 & 1 \\ MYB & 129 & 7 & 2 \\ bZIP & 115 & 3 & 4 \\ GRAS & 94 & 7 & 1 \\ Total & 1000 & 80 & 17 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Summary of transcription factor unigenes of _G. rigescens_.  
_GjWRKY1_[58]. In tomato trichomes, terpene synthase are controlled by SlMYC1 and SIWRKY73 [59]. In _Catharanthus roseus_, CrWRKY1, a regulator in biosynthesis of terpenoid indole alkaloids, interacts with transcription factors, including ORCA3, CrMYC, and ZCTs, to play a role in determining the root-specific accumulation of serpentine [60,61]. In _Nicotiana attenuata_, biosynthesis of diterpene glycosides are regulated by WRKY3 and WRKY6 [62]. In leaves of _Artemisia annua_, AaWRKY1 activated the expression of the majority of artemisinin biosynthetic genes, including _AaADS_ and _AaHMGR_[63]. In the present analysis, 141 unigenes were annotated as WRKY family transcription factors, of which 17 were more highly expressed in leaves than in roots (Table 6). qRT-PCR results showed that _GrWRKY7_ genes were more highly expressed in leaves than in roots, while it was the opposite for _GrWRKY5_ and _GrWRKY6_ (Figure 10). Thus, _GrWRKY7_ is a good candidate to study in the regulation of the biosynthesis of gentiopicroside.
# Attributes Relevance in Content-Based Music Recommendation System
## 4 Experimental Results
### Impact of a Genre
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Genres**Strategy** & _PrioritizeMFCC_ & _PrioritizeTempo_ & _PrioritizeGenre_ & _Unbalanced_ \\  & **Same** & **Different** & **Same** & **Different** & **Same** & **Different** & **Same** & **Different** \\ \hline blues & 31 & 0 & 31 & 2 & 31 & 1 & 31 & 1 \\ classical & 30 & 0 & 30 & 0 & 30 & 0 & 30 & 0 \\ country & 25 & 2 & 23 & 5 & 23 & 1 & 23 & 1 \\ disco & 30 & 5 & 27 & 3 & 30 & 3 & 30 & 6 \\ hiphop & 22 & 0 & 24 & 2 & 22 & 0 & 22 & 0 \\ jazz & 41 & 0 & 41 & 0 & 41 & 1 & 41 & 1 \\ metal & 22 & 1 & 23 & 2 & 23 & 1 & 23 & 1 \\ pop & 34 & 1 & 33 & 2 & 34 & 3 & 34 & 3 \\ reggae & 27 & 1 & 26 & 3 & 26 & 3 & 26 & 2 \\ rock & 24 & 4 & 19 & 4 & 25 & 2 & 23 & 2 \\ \hline Sum & 286 & 14 & 277 & 23 & 285 & 15 & 283 & 17 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the musical genre of the recommendation with the actual musical genre of the input song. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Genres**Strategy** & _PrioritizeMFCC_ & _PrioritizeTempo_ & _PrioritizeGenre_ & _Unbalanced_ & **No.** & **Avg.** \\ \hline blues & 3.97 & 3.52 & 3.45 & 3.58 & 31 & 3.63 \\ classical & 3.60 & 3.43 & 3.70 & 3.57 & 30 & 3.58 \\ country & 2.35 & 2.23 & 2.31 & 2.31 & 26 & 2.30 \\ disco & 3.63 & 3.41 & 3.38 & 3.25 & 32 & 3.41 \\ hiphop & 3.59 & 3.78 & 3.22 & 3.33 & 27 & 3.48 \\ jazz & 3.81 & 3.88 & 3.76 & 3.83 & 41 & 3.82 \\ metal & 3.58 & 3.00 & 3.46 & 3.54 & 24 & 3.40 \\ pop & 3.44 & 3.18 & 3.15 & 3.21 & 34 & 3.24 \\ reggae & 3.87 & 3.80 & 3.07 & 3.47 & 30 & 3.55 \\ rock & 3.32 & 2.96 & 3.16 & 3.24 & 25 & 3.17 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of music genres and average user ratings by strategy. Analyzing Table 4, we can also see that the country genre does not suffer from any specific drop with respect to the other genres. It seems that the used methods and strategies work similarly for any music genre. This reinforces the idea that either country music is more diverse and therefore more difficult to recommend, or it depends on the taste of the people making the actual recommendation.
# Data-Driven Prediction Method for Power Grid State Subjected to Heavy-Rain Hazards
## 3 Description of Data  
Heavy-rain hazards can be determined by certain weather conditions. For instance, when the precipitation over 6 h is expected to be more than 70 mm, the Korea Meteorological Administration (KMA) issues a special weather report to warn of heavy-rain-related hazards.  
The supervised ML approach generally uses previous historical data to build its prediction model. This study considers the SVM approach to predict the grid states using common weather variables. Thus, two sets of historical data, local weather and outage data, were used to analyze the power outage. All the data ranged from January 2008 to March 2018.  
In order to evaluate the present power grid state over the subsequent hours, the model required a numerical weather prediction (NWP) forecast. Therefore, this study considered publicly available weather data provided by the KMA, which could be associated with South Korea's NWP system. NWP forecasts were transformed into a grid state via a machine learning model that was trained by the historical data to learn the dependence of the output on explanatory variables.
This problem is addressed by an interdisciplinary approach, involving the development of ultrafast single-photon sensor systems, based on SPAD arrays [17; 18; 19; 20; 21], the optimization of circuit electronics to collect and manage the high number of frames (e.g., by GPU) [23; 24], the development of dedicated algorithms (compressive sensing, machine learning, quantum tomography) to achieve the desired SNR with a minimal number of acquisitions [25; 26; 27; 28]. Finally, the performances of QPI will be further enhanced by a novel approach to imaging based on quantum Fisher information [29; 30]. Treating the physical model of plenoptic imaging in the view of quantum information theory brings new possibilities of improving the setup towards super-resolution capability in the object 3D space. Having the optimal set of optical setup parameters enables object reconstruction close to ultimate limits set by nature. In this article, we will combine a review of the state of the art in the aforementioned fields with the discussion on how they contribute to the development of QPI and its application. The paper is organized as follows: in Section 2, we discuss the working principle and recent advances of Correlation Plenoptic Imaging (CPI), a technique that represents the direct forerunner of QPI; in Section 3, we present the hardware innovations currently investigated to reduce the acquisition times in CPI; in Section 4, we review the algorithmic solutions to improve QPI; in Section 5, we outline the perspectives of our future work in the context of Qu3D project; in Section 6, we discuss the relevance of our research. The work presented in this paper involves experts from three scientific research institutions, Istituto Nazionale di Fisica Nucleare (INFN, Italy), Palacky University Olomouc/Department of Optics (UPOL, Czechia), and Ecole polytechnique federale de Lausanne/Advanced Quantum Architecture Lab (EPFL, Switzerland), and from the industrial partner Planetek Hellas E.P.E. (PKH, Greece). The activity is carried within the project \"Quantum 3 DImaging at high speed and high resolution\" (Qu3D), founded by the 2019 QuantERA call [31].
## 6 Applications
The xgamma distribution suggested by [40] with pdf given by \[f(x;\theta)=\frac{\theta^{2}}{1+\theta}\bigg{(}1+\frac{\theta}{2}x^{2}\bigg{)}e^{ -\theta x},x>0,\,\theta>0.\] - Gamma distribution with pdf given by \[f(x;\alpha,\beta)=\frac{1}{\beta^{\alpha}\Gamma(\alpha)}x^{\alpha-1}e^{-\frac {x}{\beta}},x>0,\,\alpha,\beta>0.\] - Lomax distribution with pdf given by \[f(x;\alpha,\lambda)=\frac{\alpha}{\lambda}\Big{(}1+\frac{x}{\lambda}\Big{)}^{ -\,(\alpha+1)},x>0,\,\theta,\beta>0.\] - Darna distribution proposed by [41] with pdf given by \[f_{DD}(y;\theta,\alpha)=\frac{\theta}{2\alpha^{2}+\theta^{2}}\bigg{(}2\alpha+ \frac{\theta^{4}y^{2}}{2\alpha^{3}}\bigg{)}e^{-\frac{\theta y}{\alpha}};\,y> 0,\,\theta>0,\alpha>0,\] - Power Darna distribution introduced by [13] with pdf given by \[f_{PDD}(x;\alpha,\beta,\theta)=\frac{\beta\,\theta}{2\alpha^{3}(2\alpha^{2}+ \theta^{2})}x^{\beta-1}\Big{(}4\alpha^{4}+\theta^{4}x^{2\beta}\Big{)}e^{- \frac{\theta^{2}}{\alpha}},x>0,\,\theta,\beta,\alpha>0.\] - Power Lindley distribution proposed by [2] with pdf defined as \[f_{PLD}(x;\beta,\theta)=\frac{\beta\,\theta^{2}}{1+\theta}x^{\beta-1}\Big{(}1+ x^{\beta}\Big{)}e^{-\theta x^{\beta}},x>0,\,\theta,\beta>0.\] - Exponentiated power Lindley distribution (EPLD) offered by [42] with pdf given by \[f_{EPLD}(x;\beta,\theta)=\frac{\alpha\beta\,\theta^{2}\,x^{\beta-1}}{1+\theta }\Big{(}1+x^{\beta}\Big{)}e^{-\theta x^{\beta}}\bigg{[}1-\bigg{(}1+\frac{ \theta x^{\beta}}{1+\theta}\bigg{)}e^{-\theta x^{\beta}}\bigg{]}^{\alpha-1},x> 0,\,\theta,\beta>0.\]
## 3 Results
### Adasyn  
Distinguishing itself from SMOTE, ADASYN introduces an adaptive dimension to the generation of synthetic data. Unlike SMOTE, which uniformly enhances the minority class with synthetic instances, ADASYN adopts a more dynamic approach. It focuses its synthetic sampling efforts on areas of the feature space where minority instances are sparse, offering a more nuanced adaptation to the data distribution.  
ADASYN dynamically adjusted the dataset by augmenting the minority class instances to 200,721, a substantial increase, while maintaining the count of the majority class at its original level of 198,647 instances.  
The influence of ADASYN on model performance is evident in Table 7. Upon reviewing the performance metrics presented in this table, it becomes apparent that ADASYN, while enhancing the recall of the models, demonstrated a somewhat more restrained impact compared with its oversampling counterpart, SMOTE. Particularly, logistic regression and random forest exhibited notable improvement once again, achieving a commendable 55% in recall. However, decision trees and boosting algorithms displayed increased accuracy but contend with a considerably lower recall.  
Upon scrutinizing the models' performance on the training data, a notable disparity in the outcomes emerges particularly for decision trees when compared with their performance on the testing data. While the model achieved remarkably high accuracy on the training data, its performance on unseen data was notably inferior. This discrepancy raises  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Accuracy/** & **Recall/** & **Precision/** & **F1-Score/** & **AUC/** \\  & **Training Accuracy** & **Training Recall** & **Training Precision** & **Training F1-Score** & **Training AUC** \\ \hline Logistic Regression & **0.73/0.74** & **0.56/0.56** & **0.16/0.16** & **0.25/0.25** & **0.73/0.73** \\ Decision Tree & 0.84/1.00 & 0.26/1.00 & 0.17/1.00 & 0.20/1.00 & 0.57/1.00 \\ Random Forest & 0.73/0.74 & 0.56/0.56 & 0.16/0.16 & 0.25/0.26 & 0.78/1.00 \\ Gradient Boosting & 0.83/0.83 & 0.40/0.40 & 0.21/0.21 & 0.27/0.28 & 0.77/0.77 \\ XGBoost & 0.90/0.92 & 0.11/0.15 & 0.32/0.44 & 0.16/0.23 & 0.80/0.84 \\ CatBoost & 0.92/0.92 & 0.08/0.14 & 0.40/0.63 & 0.13/0.23 & 0.81/0.86 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance results after implementing ADASYN.  
Figure 5: Confusion matrix of XGBoost and SMOTE.  
concerns regarding potential overfitting, indicating that the model might have captured noise or idiosyncratic patterns that do not generalize effectively beyond the training set.  
Subsequently, we proceed with the optimization of our models, leveraging the hyperparameters recommended by the GridSearchCV and Optuna algorithms. The results of this optimization endeavor are elaborated in Table 8.  
Notably, XGBoost and CatBoost maintain a commendable AUC rate of 80%, concurrently enhancing their sensitivity to 81% and 80%, respectively, surpassing SMOTE's performance on the same models. Moreover, despite the improvement in F1-score compared with previous results, there remains scope for further enhancement.
## 4 Discussion
### Influence on Cell Viability during Osteoclastogenesis  
The ESO supernatants influenced the viability of both PBMCs and pre-osteoclasts. Thereby, the 7 d and 14 d ESO supernatants led to different changes in viability compared to the 21 d supernatants. The 7 d and 14 d supernatants reduced the viability of cultivated PBMCs but did not influence pre-osteoclasts. Supernatants from day 21 did not change PBMCs' viability but increased the viability of pre-osteoclasts. This reveals a cell stage-dependent influence on the viability through the released cytokines and chemokines from stimulated osteoblasts. PBMCs consist of multipotent progenitor cell populations with a high capacity to differentiate into different cell types [60]. In addition to this differentiation potential, PBMCs as precursors have a higher proliferation capacity than already differentiated cells [61]. ESO supernatants of 7 d and 14 d increased the differentiation potential of PBMCs compared to the fewer-differentiated cells cultivated with NSO. It is possible that the enhanced differentiation reduced the viability of the cells. This assumption can be strengthened by the reduced differentiation with 21 d and the simultaneous unchanged viability. Further investigations are necessary to confirm this assumption.  
Compared to the PBMCs, the pre-osteoclasts were further differentiated, as actin ring structures were observed in all cultivation conditions. The 21 d supernatants did not enhance the osteoclasts' activation or differentiation but increased the viability of the cells. Further, the _CASP8_ gene expression was downregulated by the 21 d supernatant. As _CASP8_ is known to be an initiator for apoptosis, reduced apoptosis can be assumed, leading to higher viability [62]. Cell cycle analysis and live dead assays can provide further information about cell viability. Especially the cell cycle analysis can reveal the proliferation possibilities and provide more details on the differentiation state of the cells [61]. Moreover, other possible cytokines could have influenced the proliferation rate. Therefore, a more comprehensive screening of the ESO supernatant can be implemented to increase the understanding of stimulation factors released from osteoblasts under electric stimulation.