esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results
### Validation of Gene Expression Levels Using qRT-PCR  
To validate the expression patterns of DEGs observed in RNA-seq datasets, 12 key genes were selected for analysis of their expression levels using qRT-PCR. These DEGs included _LOX_, _HPL_, _ADH_, _AAT_, _BCAT_, _ALDH_ and _CXE_ involved in fatty acid and isoleucine metabolism, _AFS_ and _OMT_ related to sesquiterpene and phenylpropanol metabolism, transcription factors NAC1, MYB5 and bZIP44 that could regulate the production of volatile compounds. Except for _ADH_ and _ALDH_, these DEGs all showed higher expression levels in the Jonagold apple than those in the Granny Smith apple, which were consistent with the RNA-Seq data (Figure 6). These results confirmed that the transcriptomic data in our study were accurate and reliable.  
Figure 6: Expression profiles of selected 12 DEGs determined by qRT-PCR.
## 4 Conclusions  
In summary, we propose and experimentally demonstrate a drift-insensitive self-comparison method to eliminate the self-comparison measurement error and evaluate the systematic shifts. By measuring the frequency difference between two identical clock loops, the self-comparison measurement error is (240 \(\pm\) 34) mHz using the traditional method, while it is (\(-15\pm 16\)) mHz using the drift-insensitive method. Based on the drift-insensitive self-comparison technique, we use Rabi spectrum to evaluate the collisional shift of our clock. With the DISC method, the collisional shift and the second-order Zeeman shift are evaluated as 4.54(28) \(\times\) 10\({}^{-16}\) and 5.06(3) \(\times\) 10\({}^{-17}\), respectively. The lattice light and the clock laser AC Stark shifts can also be measured by this method. The DISC method could be widely used in the evaluation of the space clocks that have less possibility to operate the clock under a condition of no drift or a linear drift due to complicated space environment. Combined with the drift compensation process, the SCME can be further suppressed even in a harsh experimental environment.  
Conceptualization, H.C.; data curation, B.L. and C.Z.; funding acquisition, H.C.; investigation, Y.W.; methodology, C.Z.; project administration, H.C.; resources, H.C.; software, C.Z.; supervision, Y.W.; validation, X.L.; writing--original draft, X.L. and C.Z.; writing--review and editing, B.L. and Y.W. All authors have read and agreed to the published version of the manuscript.  
Figure 4: The frequency gap between \(m_{\rm F}=+9/2\) and \(m_{\rm F}=-9/2\) during closed-loop operation of the clock using the DISC method. The red solid line indicates the average gap by linearly fitting the experimental data. The magnetic field drift rate is \(-4(6)\)\(\times\) 10\({}^{-6}\) mG/s extracted from the experimental data, where the uncertainty indicates the 95% confidence interval and the magnetic field drift rate agree with zero.  
**Funding:** This research is supported by the National Natural Science Foundation of China (61775220), the National Key R&D Program of China (Grant No. 2016 YFF0200201), the Key Research Project of Frontier Science of the Chinese Academy of Sciences (Grant No. QYZDB-SSW-JSC004) and the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDB21030100).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to department requirements.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Experiments
### Optical Flow Estimation  
We incorporated LGA in ARFlow [26], which is an unsupervised optical flow estimation method. The results of two-frame flow estimation on the MPI Sintel benchmarkare reported in Table 3[40] for final and clean sets using the the standard end-point-error (EPE) metric. ARFlow with LGA achieves superior performance in comparison to the other unsupervised methods on all six metrics. It surpasses ARFlow, with an EPE-all of 0.387. Qualitative results are shown in Figure 7. As highlighted by the red bounding boxes, the LGA-based method produced fewer errors than ARFlow at object edges, which shows that LGA achieves a better understanding of moving and static backgrounds by identifying non-local contexts.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**MODEL** & **SSIM (\(\uparrow\))** & **PSNR (\(\uparrow\))** \\ \hline Input (hazy image) & 0.7302 & 13.80 \\ [PERSON] et al. [35] & 0.7516 & 14.43 \\ [PERSON] et al. [36] & 0.6065 & 12.24 \\ [PERSON] et al. [37] & 0.7545 & 15.22 \\ [PERSON] et al. [38] & 0.6537 & 14.12 \\ [PERSON] et al. [39] & 0.7323 & 13.98 \\ BPPNet-reduced & 0.8482 & 18.89 \\ BPPNet-reduced with LGA & **0.8663** & **20.17** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of performance of different methods with our adaptation (BPPNet + LGA) in the dehazing task on the I-HAZE [7] dataset.  
Figure 6: Example input and ground-truth samples from the I-Haze dataset and the respective results of dehazing obtained using various methods. BPP results are presented with and without the inclusion of our LGA module [35; 36; 37; 38; 39].  
Figure 7: Results of unsupervised optical flow prediction on the MPI Sintel final-pass benchmark [40]. The first row shows the input samples, and the next two rows show the estimated flow for ARFlow [26] and LGA with ARFlow. The last two rows visualize the incurred EPE-all errors for each of the methods on the final pass. Notable differences are highlighted by red boxes.
## 3 System Model and Problem Formulation
### Problem Formulation  
As mentioned above, the offloading cost is mainly composed of latency, energy, and privacy protection consumption, which depend on the offloading locations, current channel states, bandwidth allocation, and the size of the random dummy tasks. Therefore, the offloading cost minimization problem \(\mathcal{P}\) is formulated as follows:  
\[\begin{split}\mathcal{P}:\min_{\mathbf{x},\mathbf{\alpha}}F(\mathbf{x},\mathbf{ \alpha})&=\sum_{n=1}^{N}x_{n}\Big{[}\lambda_{1}T_{n}^{SAT}+ \lambda_{2}E_{n}^{SAT}+\lambda_{3}P_{n}\Big{]}\\ +&(1-x_{n})\Big{[}\lambda_{1}T_{n}^{TC}+\lambda_{2} E_{n}^{TC}+\lambda_{3}P_{n}\Big{]}\\ s.t.& x_{n}\in\{0,1\},\forall n\in\mathcal{N}\\ \end{split}\] (10a) \[\begin{split}&\quad\quad\quad\quad\quad\quad\quad\quad \quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad \quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad  
It can be seen that problem \(\mathcal{P}\) is a mixed-integer programming problem, in which the \(0-1\) integer variable \(x\) and the continuous variable \(\mathfrak{a}\) are mutually coupled. This problem is commonly reformulated by a specific relaxation approach and then solved by powerful convex optimization techniques. However, these methods perform considerable iterations, and the original problem cannot be solved within the channel coherent time, especially when many STs simultaneously offload tasks. To tackle this dilemma, we are motivated to propose an effective low-complexity deep reinforcement learning-based task-offloading algorithm to obtain the near-optimal solution. Specifically, we adopt a DNN to map the current channel states to offloading locations, and improve it via reinforcement learning.
# Microcrack Detection Using Spectral Response Data Alone
###### Abstract  
Microcracks of depth less than 200 \(\upmu\)m in mechanical components are difficult to detect because conventional methods such as X-ray or eddy current measurements are less sensitive to such depths. Nonetheless, an efficient microcrack detection method is required urgently in the mechanical industry because microcracks are produced frequently during cold-forming. The frequency response function (FRF) is known to be highly sensitive even to microcracks, and it can be obtained using both the input data of an impact hammer and the response data of an accelerometer. Under the assumption of an impulse force with a similar spectral impulse pattern, spectral response data alone could be used as a crack indicator because the dynamic characteristics of a microcrack may be dependent solely on these measured data. This study investigates the feasibility of microcrack detection using the response data alone through impact tests with a simple rectangular specimen. A simple rectangular specimen with a 200 \(\upmu\)m microcrack at one face was prepared. The experimental modal analysis was conducted for the normal (uncracked) specimen and found-first bending mode about 1090 Hz at the X-Y plane (in-plane). Response accelerations were obtained in both at in-plane locations as well as X-Z plane (out-of-plane), and the crack was detected using the coherence function between a normal and a cracked specimen. A comparison of the crack inspection results obtained using the response data and the FRF data indicated the validity of the proposed method.  
microcrack; spectral response; impact input; nondestructive testing method; simple linear specimen
# Modal Decomposition of the Precessing Vortex Core in a Hydro Turbine Model
## 5 Conclusions  
The swirling air flow of the hydro turbine model is studied using stereo-PIV and four wall-pressure sensors. The facility allows investigating the flow dynamics for flow rates ranging from the best efficiency point (BEP) to deep part load. The focus of the study is on the modal decomposition of the precessing vortex core (PVC) that arises at part load conditions.  
Spatial Fourier decomposition is applied to the signals from the pressure sensors to identify the contributions of the azimuthal wave numbers \(m=1\) and \(m=2\) to the total level of pressure fluctuations. The proper orthogonal decomposition (POD) is applied to the antisymmetric and symmetric velocity fluctuations to extract the mode shape corresponding to the modes \(m=1\) and \(m=2\).  
Both the velocity and pressure data show a similar range where the PVC is present. The \(m=2\) oscillations are synchronized to the \(m=1\) mode and oscillate at double the frequency. However, the energy content is quite significant in the part load regime and comparable to the one of the \(m=1\) mode. This suggests that the \(m=2\) mode is not a higher harmonic of the \(m=1\) mode, but driven by the own instability mechanisms.  
To further investigate the onset of the two instability modes, we observe their saturation amplitudes as a function of the swirl number. From the fit of the Landau amplitude equation, we identify two critical swirl numbers of \(S_{cr}=0.47\) and \(0.61\) for \(m=1\) and \(m=2\), respectively.  
The identification of two individual modes, which could easily be mistaken as higher harmonics, is important to develop new concepts of active flow control, e.g., energy-efficient excitation of defined azimuthal modes (\(m=1\) and \(m=2\)) may be more effective than other known control methods of the PVC.  
Conceptualization, I.L., K.O. and V.D.; methodology, I.L., K.O., V.D. and S.S.; experimentation I.L., D.S. and E.G.; writing--original draft preparation, I.L.; writing--review and editing, K.O., V.D. and S.S.; supervision, S.A. All authors have read and agreed to the published version of the manuscript.  
This investigation was supported by the Russian Foundation for Basic Research (grant number 20-58-12012, in the part of PIV measurements). The design and installation of the experimental setup was carried out partly within the framework of a state contract with IT SB RAS. The funding of the German Research Foundation (grant number 429772199) is acknowledged.  
The authors declare no conflict of interest.
# Background Subtraction for Dynamic Scenes Using Gabor Filter Bank and Statistical Moments
###### Abstract  
This paper introduces a novel background subtraction method that utilizes texture-level analysis based on the Gabor filter bank and statistical moments. The method addresses the challenge of accurately detecting moving objects that exhibit similar color intensity variability or texture to the surrounding environment, which conventional methods struggle to handle effectively. The proposed method accurately distinguishes between foreground and background objects by capturing different frequency components using the Gabor filter bank and quantifying the texture level through statistical moments. Extensive experimental evaluations use datasets featuring varying lighting conditions, uniform and non-uniform textures, shadows, and dynamic backgrounds. The performance of the proposed method is compared against other existing methods using metrics such as sensitivity, specificity, and false positive rate. The experimental results demonstrate that the proposed method outperforms other methods in accuracy and robustness. It effectively handles scenarios with complex backgrounds, lighting changes, and objects that exhibit similar texture or color intensity as the background. Our method retains object structure while minimizing false detections and noise. This paper provides valuable insights into computer vision and object detection, offering a promising solution for accurate foreground detection in various applications such as video surveillance and motion tracking.  
background modeling; Gabor filter bank; statistical moments; texture description +
Footnote †: journal:  
0024 17 133 1
## 7 Integrated Localization and Communication
### RIS-Assisted mmWave Localization  
Therefore, mmWave signals, which are ubiquitous in future wireless networks, can be used for accurate localization as complementary to visual SLAM in the robotic community for autonomous vehicles [190]. In this light, MIMO beamforming for sending narrow-beam directional waves with angular resolution is very effective in promoting mmWave-based localization [191]. In this context, the RIS technology is effective in enhancing energy-efficient mmWave localization [192]. For example, the combination of MIMO-active beamforming and RIS-passive beamforming for vehicular localization is studied [193]. Following the employment of RIS for localization, the meta-radar and meta-SLAM concepts have been proposed [194]. Furthermore, RIS-assisted mmWave radar SLAM has been proposed for autonomous vehicles [195].  
Many works can be found in the wireless community literature that studied RIS-assisted localization for stationary and quasi-stationary UEs [196; 197]. However, the application of RIS for localization in dynamic environments with UAVs and IoVs is a trending research topic [198; 199], particularly by using deep learning techniques to tackle practical problems [200].
# Enhancing Kazakh Sign Language Recognition with BiLSTM Using YOLO Keypoints and Optical Flow
## 3 Importance of Components  
This section describes our integrated approach for recognizing dynamic gestures in Kazakh Sign Language. Our model combines three key components: YOLOv8 for spatial detection of hand keypoints, optical flow analysis for capturing motion details, and a BiLSTM for modeling temporal dynamics.
## 2 Materials and Methods
### Preliminaries
#### 2.2.2 Bloom Filters  
A Bloom filter, \(BF=\{BF[1],BF[2],\ldots,BF[m]\}\), is an array of bits of length \(m\) with random \(k\) hash functions \(\{h_{1},h_{2},\ldots,h_{k}\}\), which was proposed by [PERSON] [37] in 1970. According to the analysis by [PERSON] [38], when a set's size is \(n\) and the error rate is \(P_{err}\), we can generally set \(m\) and \(k\) as follows:  
\[m\geq-n\cdot\log_{2}e\cdot log_{2}P_{err},k\geq-log_{2}P_{err} \tag{4}\]  
Bloom filter for dataset \(X\): Each bit of the Bloom filter is initialized to \(0\). Each \(x_{i}\) in the dataset \(X=\{x_{1},\ldots,x_{n}\}\) is hashed \(k\) times as \((h_{1}(x_{i}),\ldots,h_{k}(x_{i}))\), and set \(BF[h_{u}(x_{i})]=1\) for \(u\in\{1,2,\ldots,k\}\).  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Notation** & **Meaning** \\ \hline \(pk\) & The public key. \\ \(sk\) & The private key. \\ \(ki_{i}\) & The same of private key \(sk\) distributed to the \(i\)-th participant. \\ \(c_{j,i}^{u}\) & \(c_{j,i}^{u}=ERBF_{i}[h_{u}(S_{i}[j])]\), where \(i\in\{1,2,\ldots,t\}\), \(j\in\{1,2,\ldots,n_{t}\}\), \(u\in\{1,2,\ldots,k\}\). \\ \(Comb(sh_{j,1},\ldots,sh_{j,d})\) & For \(j\in\{1,2,\ldots,nt_{t}\}\), joint decryption on \((sh_{j,1},\ldots,sh_{j,t})\). \\ \(\Delta_{i}\) & \(\Delta_{i}=\prod\limits_{j=1}^{t}\prod\limits_{j=1}^{t}\prod\limits_{j=t}^{t}\), where \(i\in\{1,2,\ldots,t\}\). \\ \(sh_{j,i}\) & \(sh_{j,i}=(c_{j})^{A_{i}\cdot sk_{i}}\), where \(i\in\{1,2,\ldots,t\}\), \(j\in\{1,2,\ldots,nt_{t}\}\). \\ \(sh_{i}\) & \(sh_{i}=\{sh_{1,i},\ldots,sh_{j,i},\ldots,sh_{n,i}\}\), where \(i\in\{1,2,\ldots,t\}\). \\ \(\times\) & Homomorphic multiplication calculations. All computations between ciphertexts in the article are homomorphic multiplicative computations. \\ \hline \hline \end{tabular}
\end{table}
Table 1: \(Cont.\)Verify whether an element \(x\) belongs to a set \(X\): If \(BF[h_{u}(x)]=1\) is held for each \(u\), \(u\in\{1,2,\ldots,k\}\), \(x\) is considered to belong to the set \(X\) at an acceptable error rate \(P_{err}\).  
Randomized Bloom filter (\(RBF\)): The \(BF\) after performing the following operation is denoted as the \(RBF\): for each element \(BF[i]\) of the \(BF\), \(i\in\{1,2,\ldots,m\}\), modify \(BF[i]=rand()\) if \(BF[i]=0\); if \(BF[i]=1\), then no other operation is performed.  
Encrypted randomized Bloom filters (\(ERBF\)): The encrypted randomized Bloom filter is denoted as \(ERBF\), where \(ERBF=\{Enc(RBF[1]),Enc(RBF[2]),\ldots,Enc(RBF[m])\}\).
## 3 Material and Methods
### GC-MS Analysis  
An Agilent Technologies 6890 GC system (Agilent Technologies Inc., Palo Alto, CA, USA) coupled with an Agilent Technologies 5973 mass spectrometer (Agilent Technologies Inc., Palo Alto, CA, USA) was applied for volatility component analysis (Supplementary Materials). A HP-5 MS capillary column (30m \(\times\) 0.25 mm \(\times\) 0.25\(\upmu\)m) was used to separate the volatiles. The temperature procedure was as following: 0-5 min, 50-50 \({}^{\circ}\)C; 5-20 min, 50-200 \({}^{\circ}\)C; 20-23 min, 200-200 \({}^{\circ}\)C; 23-27 min, 200-250 \({}^{\circ}\)C; 27-32 min, 250-250 \({}^{\circ}\)C. The splitless mode was adopted. High-purity helium was applied as the carrier gas, and the rate was 1 mL/min. The working conditions of MS were as follows: The electron ionization energy was 70 eV, the full-scan acquisition was used in the range of 10-800 amu, the ion source temperature was 230 \({}^{\circ}\)C, the emission current was 200 \(\upmu\)A, and solvent delay was set at 5 min.  
Retention indices (RI) of each volatile, a parameter for qualitative indicators of GC, were calculated using the retention value of two adjacent n-alkane standards. The preliminary identification of the volatiles was done by comparing the mass spectra with National Institute of Standards and Technology (NIST) 14.0 MS database and Wiley National Institute of Standards and Technology (Wiley NIST) 14.0 MS database. These volatiles were further confirmed by matching the reference literature records and retention time of authentic standards, as well as contrasting the calculated RI with that which was recorded in the NIST network database ([[http://webbook.nist.gov/chemistry/](http://webbook.nist.gov/chemistry/)]([http://webbook.nist.gov/chemistry/](http://webbook.nist.gov/chemistry/))) [44]. Identified volatile oils were semi-quantified by comparing the peak areas of each volatile with total peak areas. Relative percentage content of the volatile compounds was calculated via the peak area normalization method using the following formula:  
\[\text{Mi}\%=\text{Ai}/\sum\text{Ai}\times 100\% \tag{1}\]  
where Mi is the percentage content of the measured volatile i; \(\sum\)Ai is the total peak area; Ai is the peak area of the measured volatile.
## 3 Key Processes for Deep Learning-Based Seepage Field Prediction
### Comparative Framework for Deep Learning Architectures in Seepage Modeling
#### 3.4.2 Capacity Boundary Matrix
FeedForward Neural (FFN) networks require more than 10,000 sets of samples to avoid overfitting due to the large number of parameters and lack of inductive bias ([PERSON] et al. [10]). Convolutional Neural Networks (CNNs) reduce the number of parameters through local weight sharing, but they still require at least 5000 labeled images to capture spatial patterns ([PERSON] et al. [3]). Graph Neural Networks (GNNs) benefit from the ability to generalize topology and require only 1000 sets of graph structure samples to model fracture network flows ([PERSON] et al. [16]). Physical Information Neural Networks (PINNs) can achieve field predictions with an error of <10% with 500 samples ([PERSON] et al. [2]) through the regularization of the physics equations, and they are particularly suitable for scenarios where new blocks are being explored or where experimental data are limited Table 6.
4. Computational efficiency: The efficiency of model computation has a direct impact on engineering utility. FeedForward Neural (FFN) networks are suitable for real-time monitoring tasks due to their simple and highly parallelized structure, which can be trained on NVIDIA V100 GPUs in 2.1 h ([PERSON] et al. [10]). The 3D convolutional operation of Convolutional Neural Networks (CNNs) increases the computational effort but can still be trained in 5.3 h with CUDA optimization ([PERSON] et al. [3]). Graph Neural Networks (GNNs) take up to 8.7 h to train due to the irregular data access pattern of the graph structure ([PERSON] et al. [16]), and they need to be accelerated by specialized graph computing libraries (e.g., DGL). Physical Information Neural Networks (PINNs) take up to 12.5 h ([PERSON] et al. [2]) to train due to the need to compute higher-order derivatives (e.g., the second-order gradient of the pressure field) at high frequencies, and they usually require distributed training or mixed-accuracy optimization. 5. Typical application scenarios Table 7: FFN networks: Scenarios: single well water content prediction ([PERSON] et al. [10]); core porosity regression ([PERSON] et al. [20]). Advantage: simple and efficient, suitable for low-dimensional mapping tasks. CNNs: Scenarios: 2D permeability field reconstruction Table 8 ([PERSON] et al. [3]); digital core two-phase flow simulation ([PERSON] et al. [23]). Strengths: spatial pattern extraction; adaptation to pictorial data. GNNs: Scenarios: fracture network flow simulation ([PERSON] et al. [16]); well network connectivity optimization ([PERSON] et al. [29]). Strengths: unstructured topology modeling; multi-scale coupling. PINNs: Scenarios: control equation inversion ([PERSON] et al. [5]); small sample pressure field prediction ([PERSON] et al. [2]). Strengths: physical consistency guarantees; few-sample generalization.