esa-sceva/satcom-chunk-collection huggingface dataset:
# The Emerging Roles of RNA Modifications in Glioblastoma
## 3 RNA Modifications in GBM
### hnRNPC and hnRNPA2B1  
During nuclear RNA processing period, hnRNPC and hnRNPA2B1 are other types of m\({}^{6}\)A readers that can bind to unfolded RNA through an m\({}^{6}\)A structural switch mechanism, since the m\({}^{6}\)A marked RNA cannot efficiently form secondary structures because the base pairing of m\({}^{6}\)A-U is weaker of than that of A-U [105; 106]. A study on the SOX2 protein-protein interactome reveals that hnRNPC and hnRNPA2B1 can interact with SOX2 in GBM [107], suggesting that they may play key roles in maintaining the stemness of GSCs.  
Actually, hnRNPC is one of the important physiological and cancer-related regulators (including GBM) of 3' UTR processing and miRNA maturation [108]. hnRNPC has a higher abundance in higher grade GBM. Mechanically, hnRNPC directly binds to primary mir-21 (pri-mir-21) and promotes the processing of miR-21 that targets programmed cell death 4 (PDCD4), an important regulator of apoptosis and cellular survival. PDCD4 subsequently supports Akt and p70 S6 kinase (p70S6K) activation, and then promotes migratory and invasive activities, increases cell proliferation and protects GBM cells from etoposide-induced apoptosis [76].  
hnRNPA2B1 is also an important modulator of pre-mRNA processing, mRNA metabolism and transportation in cells. hnRNPA2B1 protein is highly expressed in glioma tissues, correlated with glioma grades and related to poor prognosis [75]. Mechanically, hnRNPA2B1 promotes GBM cell viability, adhesion, migration, invasion, chemoresistance to TMZ and tumorigen as well as protects cells from apoptosis and reactive oxygen species (ROS) generation possibly via downregulating tumor suppressors such as cellular FLICE (FADD-like IL-1\(\beta\)-converting enzyme)-inhibitory protein (c-FLIP), bridging integrator 1 (BIN1) and WW domain containing oxidoreductase (WWOX), and upregulating phospho-signal transducer and activator of transcription 3 (p-STAT3), matrix metallopeptidase 2 (MMP-2), and the proto-oncogene RON (also known as macrophage stimulating 1 receptor, MST1R) [74; 75]. These results indicate that hnRNPC and hnRNPA2B1 are possible m\({}^{6}\)A readers that contribute to GBM pathogenesis.  
Collectively, from the current study, YTHDC1 is the only confirmed m6A reader that exerts its function in GBM dependently on its biological function in m6A RNA modification. Whether other readers' functions are m6A-dependent remains unknown. However, most of their functions are dependent on their RNA binding capacity, implying that m6A in RNAs may be involved in their binding and their functions in GBM. This hypothesis needs to be further confirmed. As an important mark, m6A may have more readers than we can imagine. These readers are like messengers of m6A marks but beyond that, because they not only deliver message of m6A modified mRNA, but also determine their fate.
# A Survey on Intrusion Detection Systems for Fog and Cloud Computing
###### Abstract  
The rapid advancement of internet technologies has dramatically increased the number of connected devices. This has created a huge attack surface that requires the deployment of effective and practical countermeasures to protect network infrastructures from the harm that cyber-attacks can cause. Hence, there is an absolute need to differentiate boundaries in personal information and cloud and fog computing globally and the adoption of specific information security policies and regulations. The goal of the security policy and framework for cloud and fog computing is to protect the end-users and their information, reduce task-based operations, aid in compliance, and create standards for expected user actions, all of which are based on the use of established rules for cloud computing. Moreover, intrusion detection systems are widely adopted solutions to monitor and analyze network traffic and detect anomalies that can help identify ongoing adversarial activities, trigger alerts, and automatically block traffic from hostile sources. This survey paper analyzes factors, including the application of technologies and techniques, which can enable the deployment of security policy on fog and cloud computing successfully. The paper focuses on a Software-as-a-Service (SaaS) and intrusion detection, which provides an effective and resilient system structure for users and organizations. Our survey aims to provide a framework for a cloud and fog computing security policy, while addressing the required security tools, policies, and services, particularly for cloud and fog environments for organizational adoption. While developing the essential linkage between requirements, legal aspects, analyzing techniques and systems to reduce intrusion detection, we recommend the strategies for cloud and fog computing security policies. The paper develops structured guidelines for ways in which organizations can adopt and audit the security of their systems as security is an essential component of their systems and presents an agile current state-of-the-art review of intrusion detection systems and their principles. Functionalities and techniques for developing these defense mechanisms are considered, along with concrete products utilized in operational systems. Finally, we discuss evaluation criteria and open-ended challenges in this area.  
cloud computing; intrusion detection and prevention; security; recommendations for cloud computing and security; recommendations for network security; defense techniques +
Footnote †: journal: Future Internet
# A Lifetime Maximization Relay Selection Scheme in Wireless Body Area Networks
## 3 System Model
### Conception of Network Lifetime  
As discussed before, a WBAN is very different from a WSN. In a WSN, network lifetime is generally specified as the time duration between the network initialization/restart to the point when the last sensor or the majority of sensor nodes die. However, in a WBAN, each sensor node is irreplaceable due to its unique function. The exhaustion of one sensor node may lead to network failure. As a consequence, network lifetime of a WBAN in this paper is denoted as the time duration between the initialization/restart of a WBAN to the point when the first sensor node in the network exhausts.
# A Novel Real-Time Match-Moving Method with HoloLens
## 2 Background Theory  
### Match-Moving  
Match-moving uses numerical algorithms that can estimate position information by searching feature points from bright spots, dark spots, and corners. Typical examples are simultaneous localization and mapping (SLAM) [4; 5; 6] and structure-from-motion (SfM) [7; 8]. These methods extract the external parameter of the camera. There are many commercialized programs in the film industry, including Boujou [9], PFtrack [10], and Match-Mover. Figure 1 shows the work-flow of Boujou, which is a feature point-based method and a common match-moving program.  
The match-moving programs mentioned above have two main processes for virtual-real image synthesis. First, there is \"track features\", which is a process of extracting the feature points from recorded images. Secondly, \"camera solve\" is a process of tracking transformation information of the same feature points in every recorded frame to create the external parameter of the camera. Then, the obtained external parameter can be created as a virtual camera in a 3D program such as 3 ds Max.  
Most match-moving programs require stable camera movement or a stable environment (i.e., they require a cross pattern that is able to find many feature points more easily) in order to obtain accurate feature points. However, if motion blur by fast camera movement or occlusion by a large object is included in the recorded video like in Figure 2a,b, feature tracking will fail. Therefore, \"camera solve\" also cannot be generated properly. As a result, accurate virtual-real image synthesis is disturbed and much time and many human resources are wasted.  
Figure 1: The match-moving process using the Boujou method creating image synthesis by extracting feature points and camera solve from video frames.
# Self-Supervised Keypoint Learning for the Geometric Analysis of Road-Marking Templates
## 3 Methodology
### Evaluation Metrics
#### 3.7.3. Alignment Reconstruction Error  
A second key evaluation metric assesses the ability of the learned keypoints to facilitate accurate geometric alignment between a template and its transformed version. Werefer to this as the Mean-Square Error (MSE) or ARE. This metric quantifies the pixel-wise difference between a target-transformed template image and a warped-source template image after estimating the affine transformation, using predicted keypoint correspondences. For our GeoTemplateKPNet method, the keypoint correspondences are established by directly assuming that the \(k\)th keypoint predicted in the source template image corresponds to the \(k\)th keypoint predicted in the target-transformed template image, based on their channel indices and the learned class-specific keypoint ordering. With these \(K\) presumed correspondences, an affine transformation is estimated using least squares (e.g., via 'cv2.getAffineTransform' from the OpenCV library [33]). The source template image is then warped using this estimated transform. The ARE is calculated as the mean-square error between the warped-source template image and the target-transformed template image, restricted to the area within the transformed ground-truth mask of the source template. This ensures that the error calculation focuses on the aligned template region. The ground-truth mask of the source template is warped using the estimated affine transformation to define the evaluation area on the target-transformed template image.  
Let \(N_{test}^{synth}\) be the number of pairs in the synthetic test set. For the \(i\)th test pair (source template \(I_{i,src}\) and target-transformed template \(I_{i,tgt}\)), let \(P_{i,src}\) and \(P_{i,tgt}\) be the \(K\) predicted keypoints in the source and target images, respectively. Let \(T_{est,i}\) be the estimated affine transformation from \(P_{i,src}\)\(+\)\(P_{i,tgt}\) correspondences. Let \(M_{i,src}\) be the ground-truth mask for the source template and \(\Omega_{i}\) be the set of pixel locations within the warped mask area in the target image, \(\Omega_{i}=\{(h,w)\mid\text{Warp}(M_{i,src},T_{est,i})(h,w)=1\}\). Let \(I^{\prime}_{i,src}\) be the warped-source template image (\(I^{\prime}_{i,src}=\text{Warp}(I_{i,src},T_{est,i})\)). Then, the ARE is calculated as follows:  
\[\text{ARE}=\frac{1}{N_{test}^{synth}}\sum_{i=1}^{N_{test}^{synth}}\frac{1}{| \Omega_{i}|}\sum_{(h,w)\in\Omega_{i}}(I^{\prime}_{i,src}(h,w)-I_{i,tgt}(h,w) )^{2} \tag{13}\]  
where \(|\Omega_{i}|\) denotes the number of pixels in the set \(\Omega_{i}\). This metric directly quantifies the accuracy of the template-to-transformed-template alignment enabled by the learned keypoints.
## 3 Materials and Methods
### The Architecture of DARTS  
We would like to provide further clarity on the architectural intricacies of the DARTS method. The structure we employ involves a cell configuration, where each cell, apart from the stem layer, comprises a total of 4 layers. However, due to the exploration of 8 operators for each layer within the cell and the examination of connections between layers, the search space expands to 8 operators multiplied by 14 inter-layer connections. Throughout the search phase, the DARTS algorithm duplicates this cell 8 times and updates them simultaneously, with a similar duplication of 20 cells during the training process. Additionally, reduction cells were applied twice at specific intervals, corresponding to 1/3 and 2/3 of the total architecture size. This inclusion serves to refine the architecture's performance.  
The resulting search space for alpha amounts to 912 layers. Regarding the inner weights (w), during the search phase, the total layers equate to \((2+8\times 14)\times 8\), encompassing two layers for the stem cell, 8 layers in mixed operators, and 14 inter-connection layers across 8 cells. During the training process, the total number of layers is \((2+4)\times 20\). Notably, only 4 layers remain in a cell due to fixed operators, with 2 stem cell layers and 4 internal layers per cell, comprising 20 cells in the training model. It's important to acknowledge that the actual layer count can deviate from the search process's results. If a search for an inter-layer connection yields 'none', the connection can be eliminated. Similarly, if the result is'skip connect', the connection is skipped.
# On Beamforming with the Single-Sideband Transform
## 4 Comparisons and Simulations
### Comparison for Different iSERs  
We now compare the beamformers for different values of the input SER (iSER); that is, we change the signal's power in the source within the device, right next to the sensors. This simulation's results are presented in Figure 4. Overall, the accurate results for both  
Figure 3: Output metrics for the beamformers over time, for varying \(N\).  
transforms are comparable, with the SSBT-A again underperforming marginally except for a high iSER and \(L_{Y}=1\). The same results as obtained previously are observed for the estimated RFR outputs. For \(L_{Y}=1\), the SSBT-E grossly underperforms compared to the STFT-E, and although their difference for \(L_{Y}=16\) is not as substantial, it is still relevant, except for a higher iSER.  
We repeated these simulations, but instead of different values of \(L_{Y}\), we changed the number of samples per frame to \(N=512\) and set \(L_{Y}=1\), with results in Figure 5. This was  
Figure 4: Output metrics for the beamformers for varying iSERs, with \(N=32\) samples, and two cases of \(L_{Y}\).  
chosen using the knowledge from Section 2.4 and Figure 3, where we both theoretically and practically have that a higher \(N\) minimizes the effects of estimation error. Notably, the results for the STFT-A and STFT-E are identical in this scenario, as in Figure 3 for a higher \(N\). Meanwhile, the SSBT-A and SSBT-E results are similar only for very low iSERs (below \(-20\) dB), and for higher input SERs, the estimated SSBT results deteriorate drastically. Although a high \(N\) value was used, which theoretically would lead to a better SSBT-E result (following Figure 3), we see that increasing the iSER led to a worse performance, similar to the results seen in Figure 4.
# Icing Detection over East Asia from Geostationary Satellite Data Using Machine Learning Approaches
## 3 Methodology  
A flow chart of the proposed approach is provided in Figure 1. COMS MI and Himawari-8 AHI products were used as the input data. Since the satellite data have different spatial resolutions, all data were resampled to 1 km before input features were extracted based on the PIREP data. The extracted samples were used to develop icing detection models and a five-fold cross-validation method was used to evaluate the proposed models.
## 3 Investigation of Scalability and Impact of Virtualization on Performance of the System
### Testbed Description
#### 3.1.4 Evaluation Metrics  
Our goal at this point was to investigate how various numbers of cores used by the middleware affect the performance and scalability [40] of an IoT system. Subsequently, we also aimed to check if containerization influences the performance of the platform and if so, to what degree.  
Consequently, we decided to test various conditions reflected by various numbers of:  
1. cores used by the middleware,
2. IoT devices.  
Taking into account the obtained results, for a given number of cores used, we identified the load giving the largest number of successfully processed messages served within the test time frame. This is considered as the optimum load point. Such points will be different for various configurations because of the various numbers of cores involved in processing. Larger numbers of CPU cores used by a middleware will allow increasing the optimum load point in terms of the number of devices served. We took into account measures beginning with small load and ending at the aforementioned optimum load point. For such analysis we investigated the influence of:  
* number of cores on scalability,
* containerization on scalability,
* containerization on performance.
## 3 Estimation and Reliability Calculation of Road Travel Time Distribution after Earthquake
### Estimation of Travel Time Distribution after Earthquake  
The probability density of the travel time distribution of a path after the earthquake is estimated by the kernel density method. According to Formula (27), the travel time distribution function of the path after the earthquake is:  
\[P(t<T)=\int_{-\infty}^{T}\hat{p}_{post}(t) \tag{29}\]  
Calculation of travel time reliability after earthquake.  
\[R=P\{t<\hat{\mu}+T_{r}\} \tag{30}\]  
where:  
\(R\) is the reliability of travel time.  
\(t\) is the actual travel time.  
\(t\)\(\rightarrow\)\(T\), \(\hat{\mu}\) is expected value of \(T_{r}\) and m is the delay time that travelers can endure.  
Because of the uncertainty of m, it is taken directly.  
\[R=P\{t<T_{c}\} \tag{31}\]  
where:  
\(T_{c}\) is the threshold value, which generally takes the mean value \(\hat{\rho}\), 1.1 \(\hat{\rho}\) or other fixed value.
# Predictive Control for Current Distortion Mitigation in Mining Power Grids
## 4 System Implementation and Results
### Case 4: Power Capacity Test  
In this test, the DG was saturated, which meant that power requirements to satisfy harmonics compensation and active power delivery overran its power capacity. For this test, current \(I_{1}\) was increased to 1 P.U. at the load to increase the current required for harmonics compensation, and settled to 5% at the beginning of period 1. As shown in Figure 8, the compensation requirements were satisfied, with the total power supplied by the DG being lower than its capacity. Around \(t=100\) s, \(P_{0}^{*}\) increased from 0.2 P.U. to 0.4 P.U. and, therefore, \(P_{0}^{DG}\) started its transition, but currents related to compensation were adjusted once \(P_{h,max}\) was achieved, after 50 s, as shown Figure 9.  
Since the new value of \(P_{0}^{*}\) was unachievable, the DG was saturated; however, harmonics compensation at the PCC was not altered. Finally, around \(t=240\) s, the set points \(P_{h,max}\) were adjusted, according to IEEE std 1547. In this case, harmonics currents, as well as the fundamental component supplied by the DG, were rearranged at the end of period 2. The results associated to both aforementioned disturbances are shown in Figures 8b and 9. The current frequency spectrum and the time-domain signals of power and \(\alpha\beta\) currents at the PCC are shown in Figure 8c,d. Note that, although current shapes were similar in both periods, power distortion at the beginning indicated more relaxed compensation requirements than the final state. Measured current values and parameters used in this test are shown in Table 6.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Period** & \(\mathbf{I_{1}}\) & \(\mathbf{I_{2}}\) & \(\mathbf{I_{3}}\) & \(\mathbf{I_{4}}\) \\ \hline IEEE 1547 & 100\% & 1\% & 4\% & 2\% \\ \hline \(1\) & 5.1 & 0.48 & 0.068 & 0.035 \\  & 100\% & 0.941\% & 1.34\% & 0.687\% \\ \hline \(P_{0}^{\text{oc}}=2\) & \multicolumn{4}{c}{\(THD^{\text{rec}}=\)} \\  & & & 2.968\% & \\ \hline \(\lambda_{u}[1/W^{2}]\) & \(\lambda_{0}[1/W^{2}]\) & \(\lambda_{p}[1/W^{2}]\) & \(\lambda_{c}[1/W^{2}]\) & \(\lambda_{T}\) \\ \hline \(1\) & \(1\) & 1e4 & 1e8 & 1e3 \\ \hline \end{tabular}
\end{table}
Table 5: Measured currents and weighting factors.  
Figure 7: Power and currents delivered by the PCC for case 3: (**a**) Time domain PCC signals and (**b**) Frequency spectrum when IEEE std 1547 compliance was settled.
# Dual-Band Wearable Antenna Integrated with Glasses for 5G and Wi-Fi Systems
## 2 Materials and Methods
### Preliminary Design  
The antenna design was developed through numerical simulations, employing the Remcom XFdtd version 7.9 electromagnetic simulation software [20,21]. This software utilizes the finite difference time domain (FDTD) method, which is inherently capable of handling objects composed of diverse materials and has been successfully applied to model the complex electromagnetic properties of the human body.  
The initial antenna design employed an inverted F antenna (IFA) geometry. Due to design constraints, its dimensions were limited to a maximum of 14 by 82 mm. Consequently, the remaining antenna dimensions and the position of the feed point served as the primary degrees of freedom. These dimensions were iteratively adjusted to achieve optimal impedance matching within the targeted bands of 3.5 GHz and 5.8 GHz. However, given that the inverted F antenna is not inherently dual-band, it proved impossible to optimize its dimensions to meet the specified design goals.  
Figure 1 illustrates the dimensions of the antenna that exhibited the closest impedance matching performance to the desired specifications. It was first designed for a free space environment; however, even for this relaxed requirement, the design goal was not met. Figure 2 presents the current distribution on the antenna surface for 3.5 GHz, and Figure 3 shows this for 5.8 GHz. The simulated impedance matching results for this antenna are presented in Figure 4. The voltage standing wave ratio (VSWR) minima occurred at frequencies of 3.5 GHz and 5.6 GHz. For the first band, a significant mismatch was observed, with a VSWR of 2.7. While the impedance matching for the second band was improved (VSWR = 1.7), the frequency of optimal matching did not align with the intended specification. Therefore, a modification of the original F-type antenna structure was deemed necessary, and this revised design will be detailed in the subsequent sections of this article.
## 3 Results and Discussion
### Dry High-Intensity Magnetic Separation (DHIMS)
#### 3.2.1 Effect of the Magnet Roll Speed  
The effect of roll speed on the recovery of iron and alumina in the non-magnetic fraction was investigated by increasing the roll speed from 20 to 100 rpm (see Figure 6). The magnetic separation process was performed at a magnetic intensity of 8000 Gauss, feed size of 0.250 + 0.045 mm, and feed rate of 24 kg/h. The results showed that there was a decrease in Fe2O3 recovery to 32.14% in the non-magnetic fraction as the roll speed increased from 20 to 40 rpm, as shown in Figure 6. The recovery percentage began to rise as the speed increased from 40 to 100 rpm. On the contrary, by raising the roll speed to 40 rpm, the alumina recovery reached 77.28%, although a gradual decrease in the alumina recovery was observed as the roll speed increased to 100 rpm. The results revealed that increasing the roll speed increased the centrifugal force, which enhanced the selectivity of the magnetic and non-magnetic portions. However, the exceeding increase in roll speed limits the selectivity of magnetic and non-magnetic particles.  
Figure 5: XRD pattern of the Saudi low–grade pyrophyllite ore.  
Figure 6: Effect of roll speed on Fe2O3 and Al2O3 recovery% in non-magnetic fraction.