esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Optical Flow Algorithm Based on Mask-R-CNN and K-Means for Velocity Calculation
## 2 Algorithm Design
### Pyramid LK Algorithm Based on Mask-R-CNN and K-Means  
The framework has been introduced in the top of Section 2. In this part, the process of the improved algorithm is given in detail. In this paper, vehicles and people, which would often produce locally moving targets in the road taken by the UAV, are presupposed as recognizable and masked targets, as well as trees and buildings, which would not move, are under special circumstances. Therefore, when recognizing, targets such as trees and buildings are not taken as objects of recognition.  
**Step 1**: input of the normalized image captured by UAV into the main network as Figure 4.  
**Step 2**: feature extraction and generation of regions of interest.  
The image is sent to the main network to extract the data, and then the region proposal network is used to find the region of interest. Subsequently, a layer called RoIAlign is adopted, which accurately aligns the extracted features with the input to improve the accuracy of the object mask [38].  
**Step 3**: propose the box offset, the class, and the mask. The result of Mask-R-CNN is displayed in Figure 5.  
**Step 4**: determine the color of the mask. Several background pixels are sampled around the target. The RGB (Red Green Blue) values of the pixels are extracted, and then the average values of R, G and B channels are obtained respectively. These three values are used to increase the RGB value of the mask.  
**Step 5**: cover the objects that have relative motion with masks in a similar color as shown in Figure 6. The introduction of similar color masks can bring a part of the correct optical flow. There are still erroneous optical streams at the edge of the mask. If the positions of the object are ignored, plenty of the correct optical flow values are abandoned. Therefore, the playback method of changing the pixel color to benign color retains more correct optical flow value than the play  
Figure 4: Two adjacent images captured by UAV: (**a**) the first image; (**b**) the second image.  
ignoring the pixel position. The results of this method are more accurate. In this way, the similarity between pixels can be enhanced and extra pixels movement has less impact on optical flow calculation.  
**Step 6**: input two consecutive frames of processed images into the pyramid LK algorithm  
**Step 7**: by selecting appropriate N and K, the calculated optical flow direction in the N layer can be obtained from Equation (5), and the initial optical flow estimation vector of the next layer can be obtained by substituting it into Equation (6). At last, the iterative calculation of Equation (7) is carried out once to obtain the optical flow estimation vector of the original image sequence and obtain the optical flow value result [39; 40].  
**Step 8**: input the optical flow matrix calculated by pyramid LK algorithm into the K-Means clustering algorithm and select the class closest to the real optical flow value.
## 2 Materials and Methods
### Vertical Motion Estimation
#### 2.1.2 Vertical Velocity Decomposition  
After obtaining the line-of-sight (LOS) velocity of the ascending and descending geometries, the surface deformation in the whole study area was predicted via an interpolation, to create a continuous surface of deformation over the whole study area for the vertical velocity decomposition process. Several equations were used to calculate two-dimensional (2D), and 3D displacement for InSAR results [24; 25; 26; 27]. In this study, vertical deformation was retrieved by using the matrix presented in Equations (2) and (3) [28]:  
\[\left(\begin{array}{c}d_{asc}\\ d_{desc}\end{array}\right)=\;\mathrm{A}\left(\begin{array}{c}d_{vert}\\ d_{horiz}\end{array}\right) \tag{2}\]  
where  
\[\mathrm{A}=\left(\begin{array}{cc}\cos\theta_{asc}&\frac{\sin\theta_{asc}}{ \cos\theta\Delta\alpha}\\ \cos\theta_{desc}&\sin\theta_{desc}\end{array}\right) \tag{3}\]  
\(d\) is the deformation along LOS for ascending \(d_{asc}\) and descending \(d_{desc}\), and \(d_{vert}\) is the vertical motion. The term \(d_{horiz}\) refers to the projection of horizontal deformation in descending azimuth look direction, \(\theta\) is the incident angle, and \(\Delta\alpha\) is the satellite heading difference between ascending and descending mode. The estimated horizontal motion in this matrix is in the east-west direction. The north-south horizontal motion is considered negligible because of the near-polar orbit of Sentinel-1 sensors that does not allow an accurate estimation of north-south displacement [29]. Negative values of the estimated vertical velocities represent surface subsidence in the study area. However, in this study we considered areas with vertical velocity less than \(-\)2 mm/y as subsidence areas in the further analysis.
# A Review of Video Object Detection: Datasets, Metrics and Methods
## 3 Video Object Detection Methods  
For video object detection, in order to make full use of the video characteristics, different methods are considered to capture the temporal-spatial relationship. Some papers have considered the traditional methods [29; 40; 41; 42]. These papers heavily rely on the manual design leading to the shortcomings of low accuracy and the lack of robustness to noise sources. More recently, deep learning solutions have attempted to overcome these shortcomings. As shown in Figure 2, based on the utilization of the temporal information and the aggregation of features extracted from video snippets, video object detectors can be divided into flow-based [22; 27; 28; 67; 68; 69], LSTM-based [70; 71; 72; 73], attention-based [74; 75; 76; 25; 77], tracking-based [78; 26; 79; 80; 81; 82] and other methods [36; 83; 84; 85; 86; 87; 88; 89; 90]. These methods are described in more detail below.
# A Noise-Tolerant Carrier Phase Recovery Method for Inter-Satellite Coherent Optical Communications
## 3 The Noise-Tolerant Method
### Feedforward Estimation Stage  
The feedback stage provides a coarse estimate of the carrier FO and phase. However, its capacity to estimate phase noise, which follows the Wiener process, associated with the laser is constrained [29]. Consequently, a refined feedforward stage is cascaded subsequent to the first stage. This feedforward stage employs a complex phase rotator to convert phase  
Figure 6: Block diagram of the proposed noise-tolerant method.  
information into time-domain data. A block estimation approach is employed to enhance the time-domain data, facilitating noise reduction. The phase estimation obtained for the feedforward stage is calculated as follows:  
\[\hat{\theta}_{2}=\frac{1}{4}\text{arctan}\left(\sum_{k=-N/2}^{N/2-1}e^{i\psi \theta_{d}[k]}\right). \tag{12}\]
## 3 Results  
From the interactions with the industrial stakeholders in the space development programme, it was decided that the most interesting application in which to model flexibility and qualification ability against functional requirements was the development of low power and low-cost electric propulsion systems for LEO megaconstellations. This was due to the high uncertainties in both markets and operative conditions that characterize these future applications. The method proposed for model flexibility and qualification ability is shown in Figure 3.  
The process starts with the generation of alternatives to embed flexibility in an architecture, which is conducted concurrently with the identification of possible sources of uncertainty for a system. For this step,'real options' principles [24] can be applied. This step is followed by the calculation of performances (i.e., functional requirements) and unit costs of the different elements of the system. Based on the empirical study, this paper assumes that the EP manufacturers possess available and mature performance models--such as [47]--as well as unit cost and mass models [48], and reliability models [49].  
The next step is to model the flexibility of the alternatives by running a value model with uncertainty introduced in key market parameters. For this purpose, a surplus value model [18] is adopted. The surplus value model is a means to aggregate both functional requirements (e.g., thrust) and non-functional requirements (e.g., flexibility, qualification ability) onto the same financial metric. Also, the surplus value model aggregates the cost and duration of the qualification activities, modelled using a discrete-event qualification technique. The final steps include the visualization of the results, where decisions are made. The following sections present the application of the method on an industrial case. As stated in the introduction, the purpose of this case study is to demonstrate the methodology in an industrially relevant set-up, rather than a detailed comparison between alternative propellant systems.
# A Method to Estimate Dynamic Pore Water Pressure Growth of Saturated Sand-Gravel Materials
###### Abstract  
The liquefaction of saturated sand-gravel material samples from the Xinjiang valley were investigated under cyclic loading. A series of large-scale dynamic triaxial tests were used to determine the dynamic characteristics of the sand-gravel sample under different confining pressures, consolidation stress ratios, and stress levels. A suitable pore water pressure growth model is proposed for the sand-gravel and sand materials. The number of cycles required to cause liquefaction was an important parameter in the dynamic pore water pressure growth model. A method to determine the number of cycles was proposed and verified by a large number of experimental data. The presentation of the pore water pressure simulation results demonstrates that the proposed pore water pressure growth model accurately characterizes the dynamic pore water pressure development in sand-gravel under cyclic loading and is also applicable to sand. The proposed pore water pressure growth model can be used to study the anti-liquefaction characteristics of foundation and dam materials of high earth-rock dams and high sand-gravel dams on deep overburdens.  
large-scale dynamic triaxial test; liquefaction; pore water pressure estimation method; number of cycles model +
Footnote †: journal: Applied Sciences
# Gaussian Process Model-Based Performance Uncertainty Quantification of a Typical Turboshaft Engine
## 2 Modeling of the Turboshaft Engine Performance
### Turboshaft Engine Performance Simulation Model
The details of the equations are presented as follows:  
1. The power balance equation for compressor and HPT is: \[P_{HPT}\eta_{HPR}=P_{COM},\] (4) where \(P_{HPT}\) denotes the power provided by the HPT, \(P_{com}\) denotes the power required by the compressor, and \(\eta_{HPR}\) is the mechanical efficiency of the HPR. 2. The flow continuity equation for compressor and HPT is: \[W_{COM}=W_{HPT},\] (5) where \(W_{com}\) denotes the mass flow in the compressor, and \(W_{HPT}\) denotes the mass flow in the HPT. 3. The flow continuity equation for HPT and LPT is: \[W_{HPT}=W_{LPT},\] (6) where \(W_{LPT}\) denotes the mass flow in the LPT. 4. The pressure balance equation at the outlet of nozzle is: \[P_{NOZ}=P_{amb},\] (7) where \(P_{NOZ}\) denotes the static pressure at the outlet of the nozzle, and \(P_{amb}\) denotes the ambient pressure. Given the ambient condition, operating condition, control scheme and undetermined parameters, all the parameters in Equations (4)-(7) can be obtained by performing calculation of the thermodynamic cycle. Let \(e_{1}\), \(e_{2}\), \(e_{3}\), and \(e_{4}\) denote the residuals of \(P_{HPT}\eta_{HPR}-P_{COM}\), \(W_{COM}-W_{HPT}\), \(W_{HPT}-W_{LPT}\), and \(P_{NOZ}-P_{amb}\), respectively. Thus, the problem of choosing \(Z_{com}\), \(Z_{HPT}\), \(Z_{LPT}\), and \(W_{f}\) is mathematically formulated as follows:  
\[\mathbf{E}=f(\mathbf{x}_{D})=\mathbf{0} \tag{8}\]  
where \(\mathbf{x}_{D}=\left[Z_{com},Z_{HPT},Z_{LPT},W_{f}\right]^{T}\) denotes a vector of undetermined parameters, \(\mathbf{E}=\left[e_{1},e_{2},e_{3},e_{4}\right]^{T}\) denotes a vector of residuals, and \(f(\cdot)\) denotes the thermodynamic cycle calculation model. Since the analytical solutions of Equation (8) cannot be derived, a numerical method, i.e., the multi-dimensional Newton-Raphson iteration method [6, 27], is employed to obtain the optimal solutions. With the optimal values of the undetermined parameters, the off-design point performance of the turboshaft engine, such as output power and specific fuel consumption (SFC), can be obtained. \begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Notation** & **Undetermined Parameters** \\ \hline
1 & \(Z_{com}\) & The location of the operating point of compressor \\
2 & \(Z_{HPT}\) & The location of the operating point of HPT \\
3 & \(Z_{LPT}\) & The location of the operating point of LPT \\
4 & \(W_{f}\) & The fuel flow \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The undetermined parameters in the simulation model.
## 5 Experimental Results and Discussion
### Digit Recognition Results using Deep Transfer Learning  
#### 5.3.1 Applying DTL on HODA and AHDBase Datasets  
We first trained the CNN model for the training part of the datasets (60,000 images) and tested in the remaining images (10,000 test image) for validating our model. We obtained above 99% accuracy for both datasets (99.47% for HODA Dataset and 99.34% for AHDBase dataset respectively). After that, we applied the DTL method for feature extraction and tested different classifiers with our local dataset. The digit recognition results by applying different classifiers on the features extracted from the local datasets are presented in Table 4. The results of AHDBase are always higher than HODA dataset because their number representation is similar to our dataset. However, since the HODA dataset is created in Iran, the number representation is different from our dataset (see numbers in Figure 9). Zero corresponds to our five, six is similar to two in our historical manuscripts, four and five are totally different in our dataset which is responsible for relatively lower accuracies. A maximum of 72.4% accuracy is achieved by using MLP classifier in AHDBase features. MLP is the most successful classifier on both datasets. We interpreted this results as the neural network structure helps MLP to learn CNN extracted features better. RF and kNN are other successful classifiers on these features for both datasets. We also extracted the same 128 features from our local dataset and tested the accuracies and area under ROC curve results (see Table 5). The accuracies are higher than the DTL results from the modern datasets (AHDBase and HODA) which shows that they could not capture the properties of these historical manuscripts successfully via the DTL method.  
\begin{table}
\begin{tabular}{l l l l l} \hline
**IoU (\%)** & **NSE (\%)** & **Pixel-Wise Precision** & **Pixel-Wise Recall (\%)** & **Pixel-Wise F-Measure (\%)** \\ \hline
49.82 & 3.94 & 0.7089 & 0.7535 & 0.7247 \\ \hline \end{tabular}
\end{table}
Table 2: The performance of our numeral spotting model (numerals vs. background) is presented with different metrics.  
\begin{table}
\begin{tabular}{l l l l} \hline
**Classification Type** & **PW Precision** & **PW Recall (\%)** & **PW F-Measure (\%)** \\ \hline Numerals vs. Background & 0.4753 & 0.8704 & 0.6126 \\ \hline Numerals and Updates vs. Background & 0.6164 & 0.7538 & 0.6751 \\ \hline Updates vs. Background & 0.9009 & 0.1574 & 0.2681 \\ \hline \end{tabular}
\end{table}
Table 3: The performance of our 3-class numeral spotting model (numerals and updates vs. background, numerals vs. background, updates vs. backgrounds) is presented with different metrics. PW stands for pixel-wise.  
Figure 9: HODA representations for Arabic digits are demonstrated [40].
## 8 Conclusions and Future Work  
This research attempts to break through the obscurity and build the body of knowledge related to CC security. The security issues of CC discussed include service misuse, insider attacks, insecure and unreliable applications, data corruption or leak, and shared technology vulnerabilities. Meanwhile, the challenges of CC discussed cover the technological, technical, and security aspects.  
This research reviewed the latest trends in optimal DDoS attack detection techniques from 2013 to 2021 and has also brought up some crucial aspects of multiple-layer attacks. This research proposed a new taxonomy of attacks, DDoS attacks, and DDoS attack detection approaches on CC. More precisely, we discussed various attacks on CC, such as suffocating, protocol, economic, and permanent economic attacks. Also covered are DDoS attacks on CC utilizing SYN floods, UDP floods, ping of death, ICMP floods, and HTTP floods. We discussed the different approaches of DDoS attack detection methods based on the signature, anomaly, hybrid, entropy, and filtering tree. In addition, we also discuss the various factors, such as efficiency, accuracy, and lightweight, that must be considered when choosing an effective DDoS attack detection strategy.  
Our observation of the proposed taxonomies reveals that anomaly-based detection approaches produced the best result due to their ability to identify anomalous events, allowing the creation of rules to reduce the false alarm rate for known and unknown attacks. However, further research on anomaly-based approaches using ML and DL is worth pursuing as they are not yet widely employed to detect HTTP-GET flood attacks on CC. Furthermore, the proposed taxonomy can serve as a roadmap for new researchers to easily explore the existing DDoS attack detection techniques on CC.  
Finally, it is worth mentioning that the proposed taxonomy has to be regularly updated to include emerging attacks.  
Writing--original draft preparation, Z.R.A. and M.A.; writing--review and editing, Z.R.A., M.A., P.J., I.H.H., T.A.A.-A.; project administration, Z.R.A.; resources, Z.R.A. and M.A.; supervision, M.A. and M.M.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Universiti Sains Malaysia under an external grant (Grant Number 304/PNAV/650958/U154).  
Not applicable.  
We would like to express our gratitude to Universiti Sains Malaysia (USM) for all the support and facilities that enable the completion of this research.  
The authors declare that they have no conflict of interest to report regarding the present study.
# Evolution of V2X Communication and Integration of Blockchain for Security Enhancements
## 3 Evolution of V2X Technologies
### Evolution of Non-Cellular V2X from 802.11p to 802.11 bd
In 802.11 bd, the Task Group bd (TGbd) introduced an adaptive re-transmission system, where frame re-transmission decisions are based on the level of congestion and the number of re-transmissions [28]. 3. Alternate OFDM Numerologies: the OFDM performance is based on the ratio of usable symbol length to overall symbol length. The OFDM efficiency increases as subcarrier spacing  
Figure 4: Frame format used for re-transmission in 802.11 bd. [27] \begin{table}
\begin{tabular}{l l l} \hline \hline
**Features/ Mechanisms** & **802.11 bd** & **802.11p** \\ \hline Frequency band & 5.9 GHz/60 GHz & 5.9 GHz \\ Sub-carrier spacing & 312.5 KHz/156.25 KHz/78.125 KHz & 156.25 KHz \\ Channel coding & LDPC & BCC \\ Re-transmission & Congestion dependent & None \\ Cyclic Prefix (CP) & 1.6 us and 3.2 us & 1.6 us \\ Spatial streams & Multiple & One \\ Relative vehicle speed & 500 km/hr & 252 km/h \\ Doppler shift counter measures & High density midambles & None \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Feature comparison between 802.11 bd and 802.11p. decreases since the duration of the cyclic prefix becomes invariant to the duration of the symbol. To improve OFDM performance, the 802.11 TGbd participants are investigating the use of narrow OFDM numerologies (i.e., subcarrier spacing) to maximize the number of subcarriers while also maintaining a 10 MHz channel [29]. Nevertheless, the specification of alternative OFDM numerologies should take into account the maximum relative velocities. 4. Dual Carrier Modulation (DCM): the DCM is a method used in 802.11 ax that includes transmitting the same symbol twice over sufficiently distant sub-carriers to achieve frequency diversity [28]. Because each transmission of symbols is repeated over two different sub-carriers, the order of modulation must be doubled to maintain the throughput. The DCM can help boost the efficiency of block-error-rate (BLER) given the rise in modulation order. 5. mmWave Frequencies: the millimeter waves (mmWaves) are usually classified as electromagnetic (radio) waves that lie within the 30-300 GHz frequency spectrum and that can communicate over short distances, but with very high throughput even at lower order MCS (such as video streaming, HD three-dimensional (3D) maps downloading, etc.). The principle for designing mmWave 802.11 bd can be legacy 802.11 standards, such as 802.11 ad, or its 802.11 ay enhancement that already operates within the mmWave bands. 6. Other PHY & MAC mechanisms: other characteristics of the PHY layer being considered for use in 802.11 bd include the use of Low Density Parity Check (LDPC) codes and multiple transmit/receive antennas to improve the reliability through multiple antennas or increase throughput by spatial multiplexing [24; 29]. The 802.11 bd standard will leverage the contention parameters of 802.11p with multiple Enhanced Distributed Channel Access types to ensure equitable and fair channel access options for 802.11 bd and 802.11p users at the MAC layer.
# RAC1B: A Rho GTPase with Versatile Functions in Malignant Transformation and Tumor Progression
## Conclusions and Perspectives
### Potential Prognostic and Therapeutic Use of RAC1B  
Despite these unsolved issues, RAC1B may still serve as a prognostic marker and a potential target in several tumor entities in which overexpression has been confirmed [40; 68]. Thus, determination of the RAC1/RAC1B balance in a biosensor-based system of real-time analysis by using blood serum of NSCLC patients revealed increasing RAC1 and RAC1B protein levels according to the progressive stage of this disease [71]. In CRC, RAC1B overexpression was revealed as a marker of poor prognosis in K-RAS/B-RAF wild-type mCRC patients treated with first-line FOLFOX/XELOX therapy [70]. Moreover, in PDAC both the expression level in the ductal epithelial/cancer cells as well as the subcellular distribution of RAC1B in these cells can have prognostic value with respect to patient outcome [19; 40; 68].  
Therapeutic targeting of RAC1B to inhibit its expression or to downregulate its activity could represent a promising approach in several types of tumors. Blocking the activity of RAC1B and its associated signaling pathways or shifting the endogenous RAC1B/RAC1 balance toward RAC1 appears a rational treatment of primary tumors with high RAC1B expression such as those of the breast, colon and lung. Even prospectively inhibiting RAC1B expression/activity in the premalignant state, e.g., during intestinal mucosal inflammation, chronic pancreatitis, or in HMEC could be beneficial since it may prevent escape from a TGF-\(\beta\)-induced senescence program with subsequent neoplastic development. Conversely, in advanced stages of PDAC where tumor progression is driven by TGF-\(\beta\), promoting RAC1B expression or shifting the RAC1B/RAC1 balance towards RAC1B could be feasible [40]. For therapeutic interventions several promising agents have been identified, i.e., four other berberine/phenantiridine/isoquinoline derivatives [67], EHT 1864 [65], and ibuprofen [27]. Future efforts should focus on the design of protein-protein interaction inhibitors that could block RAC1B-specific downstream signaling.  
Writing--original draft preparation, H.U., R.H.; writing--review and editing, C.M., R.H., H.L.; visualization, C.M., H.U.  
This research received no external funding. The APC was supported by Hannover Medical School funds to [PERSON].  
The authors declare no conflict of interest.