esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Evaluation of the Number of the Propagation Modes with DL  
To evaluate the \(M\) estimation procedure using the DL technique, we performed a simple simulation because it is difficult to know ground truth of \(M\) in experimental study. We assumed that three waves with \(k=1000,2000\), and \(3000\) rad/m were propagating. The signal-to-noise ratio (SNR) was \(40\) dB, \(N_{\rm R}=28\), and \(N_{\rm sub}=15\).  
The estimation results showed step-like changes with increasing \(\eta_{1}\). Within the \(-100\) dB \(\leq\eta_{1}\leq-90\) dB, \(-80\) dB \(\leq\eta_{1}\leq-20\) dB, and \(-10\) dB \(\leq\eta_{1}\leq 0\) dB ranges, the corresponding estimated \(M\) values were \(14\), \(3\), and \(0\), respectively. The root mean square error (RMSE) of the wavenumber estimation process with \(\eta_{1}\) that gives the correct \(M\) is \(0.73\) rad/m. An important point that should be noted here is that the true \(M\) value is estimated using a wide range for \(\eta_{1}\), e.g., \(-80\) dB \(\leq\eta_{1}\leq-20\) dB.
# An Efficient Approach for LBS Privacy Preservation in Mobile Social Networks
## 2 Related Work
### Technologies of LBS Privacy Preservation  
From the perspective of technologies that most recent works proposed for protecting privacy in LBS, they can be roughly classified into two main categories: Obfuscation-based approach and cryptographic-based approach. Obfuscation-based approach is mainly used to confuse LBSP. For this purpose, one way is to distort information based on the original and real users themselves [10; 11; 12], such as adding noise in the contents of users' requests or expanding the area of users' real locations. Another way is to use some methods to confuse LBSP through a combination with other users, such as Mix zone [13], spatial cloaking [3], temporal cloaking [14; 15], and Dummy [16; 17]. Cryptographic-based approach is mainly used to make users' information (include location, preference, etc.) invisible for adversaries when users send requests to LBSP. Recently, Homomorphic Encryption for privacy protection is a research hotspot, which can prevent the disclosure of users' information and achieve a more stringent privacy protection. For example, in paper [18], the POI (i.e., the point of interest) could be calculated by LBSP without actually knowing users' locations. The BV Homomorphic Encryption (BVHE) was used in paper [8] to calculate users' request service vector in aggregation process. However, though a stringent privacy protection can be provide by BVHE, service efficiency cannot be ignored. Thus, how to improve service efficiency while protecting users' privacy (i.e., location privacy, preference privacy) is still an important problem that needs to be addressed.
## 3 Results
### Evaluation of SPME Procedure Parameters
#### 3.4.3 Evaluation of Desorption Parameters  
The desorption time and solvent were optimized to ensure complete recovery of analytes from the SPME Arrow after extraction. Various desorption times (from 1 to 40 min) and solvents (acetonitrile, acetone, methanol, acetonitrile/water = 9/1 (\(v/v\)), acetonitrile/water = 7/3 (\(v/v\)), and acetonitrile/water = 1/9 (\(v/v\))) were tested. Among the solvents, acetonitrile exhibited the highest desorption efficiency due to its lower polarity compared to methanol and its stronger elution efficiency as a polar protonic solvent (Figure 5f) [41]. As a result, acetonitrile was selected as the optimal desorption solvent. Subsequent optimization of desorption time revealed that 30 min was sufficient to achieve maximum desorption of the target analytes (Figure 5g). This desorption time is relatively long because static-state desorption mode was utilized, but it is still faster than the reported method, which needs 35 min even when agitated desorption mode is utilized [37]. In general, the shortcoming of the long desorption time of static-state desorption can be compensated by a higher enrichment factor and coating lifetime because much less desorption solvent is used, and less mechanical destruction occurs during the agitation.
# Semantic 3D Mapping from Deep Image Segmentation
## 3 Segmentation and Mapping in 3D
### 3D Segmentation
**[lines 10,11]**: If the expansion point is already in the octomap output recursion to this point is stopped. If not, the recursive function is called. Figure 4 shows the application of this algorithm in a point cloud. For simplicity and better visualization, we have removed one dimension, so we will see a 2D projection of Algorithm 1. Let us imagine that this is a detection seen from above (Figure 4a), in which the detected object corresponds to the blue points. Red dots are errors in detection:  
1. In Figure 4b, the starting point is established and the neighbor search is performed. 2. In Figure 4c, when finding neighbors (orange points from the original point cloud), their position is added to the output octomap (green box), new search points (green points) are created and the process is repeated in each of them. 3. Any successful searching produces a new cell in the output octomap, and generate new search points (Figure 4d). 4. The process is repeated in Figure 4e-g, until the detection limits are reached, or until there are no new search points that can be generated (green points with no candidates within the search radius). 5. The final result (Figure 4h) is an octomap that includes the points that really belong to the object to be detected. Outliers are not included in this octomap. ```
1:function\(3D\_Segmentation(input\_image,input\_cloud)\)
2:\(2d\_detections\gets deep\_2d\_segment(input\_image)\)
3:\(detection\_octomap\leftarrow\emptyset\)
4:for all\(detection\in 2d\_detections\)do
5:\(KD-tree\gets get\_KD-tree(input\_cloud,detection)\)
6:\(start\_point=to\_3D(detection\_center.x,detection.center.y)\)
7:\(expand\_octomap(start\_point,KD-tree,octomap)\)
8:functionexpand_octomap(point, KD-tree,octomap)
9:\(kr\gets KD-tree.resolution\)
10:\(neighbours\gets KD-tree.radius\_search(point,kr/2)\)
11:if\(neighbours\
eq\emptyset\)then
12:\(octomap\leftarrow\{point,KD-tree.color\}\)
13:for\(dx\leftarrow\{-kr,kr\}\)do
14:for\(dy\leftarrow\{-kr,kr\}\)do
15:for\(dz\leftarrow\{-kr,kr\}\)do
16:\(new\_point=\{point.x+dx,point.y+dy,point.z+dz\}\)
17:if\(new\_point\
otin\)\(octomap\)then
18:\(expand\_octomap(new\_point,KD-tree,octomap)\)
```  
**Algorithm 1** 3D Segmentation  
Figure 4: Example of the application of the algorithm with a simplification in 2D.
## 4 Experimental Results
### Ablation Study
When attention is calculated within the range of the M \(\times\) M \(\times\) M local window, the \(\text{r}^{\text{v}}_{\text{a}-\text{i}\text{b}-\text{j}\text{c}-\text{k}}\) vector is added, through which more position information can be obtained, and since they are shared between the attention heads between each layer, no additional parameters are introduced. The BraTS 2019 validation set is a subset of the BraTS 2019 dataset that does not come with specific labels. It is used to evaluate the performance of algorithms in brain-tumor segmentation tasks. Segmentation results need to be uploaded to the official website for evaluation. Table 5 shows the segmentation accuracy of the model when position-free coding information, relative position coding, and our proposed DualTrans position coding are adopted into the BraTS 2019 verification set. It can be seen that the segmentation accuracy of the model decreases significantly when there is no position information. When the relative position coding information is used, the segmentation accuracy of the model is greatly improved. When our proposed DualTrans position information is used, the Dice coefficient of the relative position coding model is increased by 0.17%, 0.14%, and 0.18% in the ET, TC, and WT regions, respectively. The results show that adding the DualTrans location information in the M \(\times\) M \(\times\) M window is helpful for improving the accuracy of brain-tumor segmentation. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Dice Score (\%)**} \\ \cline{2-4}  & **ET** & **TC** & **WT** \\ \hline
3D-UNet & 70.86 & 72.48 & 87.38 \\ DualTrans (ours) & 86.23 & 88.12 & 92.83 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ablation study on cnn encoder (BraTS 2019).
# Blockchain-Based Federated Learning: A Survey and New Perspectives
## 4 Blockchain-Based Federated Learning: State-of-the-Art
### Application Direction of Blockchain-Enabled Federated Learning
#### 4.2.4 Robust Mechanisms  
Robust mechanisms refer to system efficiency performance mechanisms. Federated learning is a machine learning framework specifically for data privacy protection. In traditional federated learning, the system cannot fully control the impact of client failures on model training, resulting in poor model training results. Existing research enhances the robustness of federated learning by leveraging the decentralization and immutability of blockchain.  
With the support of blockchain, the efficiency and reliability of federated learning have been greatly improved. For example, the use of a trusted and efficient decentralized federated slicing architecture [108], combined with reinforcement learning to accelerate optimization of resource allocation, improves federated learning efficiency. In addition, leveraging model compression to improve modeling efficiency [93] can make the federated learning modeling process more robust. In [109], a blockchain-based federated learning parameter update architecture PUS-FL is proposed to solve the problem of model accuracy degradation caused by the existing traditional gradient filtering.
## 6 Future Research Scope  
### Scaling Improvement  
In satellite communication, scaling is the property that a model can be scaled for different input values. A model supporting a scaling property can be used for parameters with different frequencies, polarization techniques, elevation angle, and path length scaling. A model developed in a temperate climate region cannot be directly applied to a tropical area unless modified somehow. The proper scaling mechanism regarding these parameters is yet to be devised. The ITU-R model is the most significant model that has been moderated for tropical regions by many researchers [142; 143; 144; 29; 145; 146]. However, these models do not support the scaling properties. There is research scope for adding various scaling properties to these models.
# Using Satellite-Derived Vegetation Products to Evaluate LDAS-Monde over the Euro-Mediterranean Area
## 2 Material and Methods
### Satellite Observations
#### 2.3.2 Evapotranspiration  
[PERSON], [PERSON] et al. [29] produced global monthly estimates of the land-surface evapotranspiration from multiple satellite-based products from 1980-2016. They used the GLEAM (Global Land surface Evaporation: the Amsterdam Methodology) approach, mainly driven by microwave remote sensing observations while also constrained by satellite-derived soil moisture products. The GLEAM v3.1 product (with several algorithm improvements described in [50]) was used in this study at a spatial resolution of 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) and averaged at the model resolution of 0.5\({}^{\circ}\). Figure 1 shows the average evapotranspiration product for the 2007-2015 period considered in this study. Pixels too close to the coasts and pixels where the vegetation coverage was too low (less than 10% of the pixel covered by any kind of vegetation according to ECOCLIMAP) were filtered out.  
Figure 1: Averaged satellite-derived products for the whole period from 2007–2015: (**a**) original Soil Water Index (SWI); (**b**) Leaf Area Index (LAI); (**c**) evapotranspiration; (**d**) Gross Primary Production (GPP) and (**e**) Sun-Induced Fluorescence (SIF).
## 4 Proposed Frequency-Offset Estimation Method  
In this section, a computationally-efficient CFO and SFO estimation scheme is proposed in the DVB-C2 OFDM system using CPs as pilots. To this end, the LSE scheme is realized using a block-by-block correlation that it is not computationally expensive, in comparison to the existing methods. To verify the performance of the proposed LSE approach, the MSE is considered as a performance measure, and an optimal block size to minimize the MSE is calculated.
# Analysis of Fire Resistance of Prestressed Concrete T-Beam Based on ABAQUS Numerical Simulation
## 3 Calculation Results and Parameter Analysis of Fire Resistance of Prestressed Concrete T-Beam
### Analysis of Parameters Affecting the Fire Resistance of Prestressed Concrete T-Beams  
#### 3.3.1 Effect of Different Fire Modes on Fire Resistance  
Figure 9 shows the curves of the variation of the mid-span deflection of the prestressed concrete T-beams under different fire modes. The variation of mid-span deflection of prestressed concrete T-beams under different fire modes shows a large variability. As the fire time increases, the rate of decrease of the curve increases with the increase of the fire surface, and the difference of the mid-span deflection at the same moment becomes larger. The increase in the fire surface results in a faster temperature transfer in the prestressed concrete T-beam. The higher the temperature of the concrete, steels, and prestressing bars simultaneously, the faster the mechanical properties decrease. Thus, the more likely it is that the structure will reach its fire resistance. Figure 10 shows the variation of the fire resistance for different fire modes at load ratios \(n=0.4\), \(n=0.6\), and \(n=0.8\). An increase in the load ratio leads to an increased rate of decrease in the fire resistance of prestressed concrete T-beam because the load exacerbates the damage to the structural material under the action of high temperatures.
## 2 The Applications in Smart Grid Based on Time Synchronization
### Synchrony Measurements  
Various devices are strategically deployed to enhance the security, reliability, and efficiency of the power system. One such critical device, the Phasor Measurement Unit (PMU), monitors essential situations, including supply interruptions, load variations, contingencies, and faults [37]. A PMU produces synchronized estimates of phasors, frequency, and rate-of-change derived from voltage and/or current signals, accompanied by a time-synchronizing signal. A synchrophasor represents the magnitude and instantaneous phase angle of a signal relative to a cosine function at the nominal system frequency, synchronized with UTC [38].  
PMUs are increasingly utilized to deliver synchronized measurements for system situational awareness and dynamic monitoring. Synchrophasor data, being time-sensitive, must be accurately time-stamped and promptly transmitted [39]. To align synchrophasor data from multiple PMUs, a Phasor Data Concentrator (PDC) uses the timestamps associated with each measurement [40].  
Synchrophasor applications demand the highest precision in timing requirements across the power system due to frequent reporting, phasor precision needs, and the wide geographic distribution of PMUs [32]. IEEE C37.118.1-2011 [41] specifies a maximum 1% Total Vector Error (TVE), encompassing both magnitude and phase errors. TVE is the total uncertainty in the measurement result due to both magnitude error and phase error. For a 60 Hz system, a 1% phase error corresponds to a timing error of approximately 26.5 us, while for a 50 Hz system, it corresponds to around 31.8 us. However, to accommodate other sources of uncertainty, the standard suggests a maximum timing uncertainty of 1 us instead of 26.5 us.
## 4 Simulation Results and Discussion
### KPI Evaluation and Optimization Using Brute Force
#### 4.1.1 Performance Evaluation When GUEs Are Distributed Using MCP  
For a varied number of UABS deployed in the AG-HetNet, we plot the 5 pSE variation for a positive bias CRE as seen in Figure 8a-c. When 50% of the MBSs are destroyed, the peak values in the case of the no-ICIC mechanism (NIM), elCIC, and FeICIC observed lower values of CREs. Moreover, as the CRE increases, the number of GUEs associated with the UABSs increases, and so does the interference experienced by these GUEs, thus decreasing the overall 5 pSE. In a high disaster-struck scenario, when 97.5% of the MBSs are destroyed, a relatively slow degradation of peak values of 5 pSE is observed with the increasing CRE for NIM, elCIC, and FeICIC techniques.  
Figure 11: Peak 5 pSE performance of the wireless network; when GUEs are distributed using TCP, UABS deployed on a fixed hexagonal grid, and for different ICIC techniques. (**a**) NIM Peak 5 pSE vs. CRE. (**b**) elCIC Peak 5 pSE vs. CRE. (**c**) FeICIC Peak 5 pSE vs. CRE.  
Figure 12: Peak coverage performance of the wireless network; when GUEs are distributed using TCP, UABS deployed on a fixed hexagonal grid, and for different ICIC techniques. (**a**) NIM Coverage probability vs. CRE. (**b**) elCIC Coverage probability vs. CRE. (**c**) FeICIC Coverage probability vs. CRE.  
Figure 13: Performance comparison when GUEs are distributed using TCP and UABS deployed on a fixed hexagonal grid.  
In Figure 9a-c, we plot the coverage probability variation for a positive-biased CRE. When 50% of the MBSs are destroyed, the peak values of NIM, eICIC, and FeICIC are observed for the moderate values of positive-biased CREs. Moreover, as the CRE increases, the number of GUEs associated with the UABSs increases, and so does the interference experienced by these GUEs, thus decreasing the overall 5 pSE. In a high disaster-struck scenario, when 97.5% of the MBSs are destroyed, the peak value of 5 pSE for NIM is observed at around 0 dB CRE, the eICIC technique observes a peak value in the range of 10-12 dB CRE, and FeICIC is observed in the range of 0-3 dB CRE.  
Following the comparative analysis of KPIs shown in Figure 10a,b, we observe a minor deviation in the peak values of 5 pSE between NIM, eICIC, and FeICIC when 50% of MBSs are destroyed--a modest deviation in the peak values when 97.5% of MBSs are destroyed. In comparison, we observe a modest deviation in the peak values from NIM to eICIC and a minor deviation in the peak values from eICIC to FeICIC for coverage probability in both scenarios of 50% and 97.5% of the MBSs being destroyed.
## 3 Methodology
### Data Preprocessing  
A total of 16 electrodes out of 32 were selected from channels 11-26. These channels are close to the three channels of interest associated with motor movements: C3, Cz, and C4 [52]. A Butterworth band-pass filter with a passband range between 7 and 30 Hz was applied, as motor movements primarily involve sensory motor signals within the mu and beta bands [10]. The Butterworth filter was chosen for its ability to provide a smooth passband output. This filtering process also removes unwanted artifacts such as powerline interference, ocular artifacts, and electrocardiogram (ECG) and electromyogram (EMG) signals [17; 28]. Z-score normalization was then applied by subtracting the mean and dividing by the standard deviation. In this section, five models built for the classification are described. As mentioned at the start of this section, these five systems are as follows: preliminary study model, baseline system, ML models 1 and 2, and ML-CSP-OVR.
## 5 Experimental Signal Analysis  
In this study, the root cause of vibrations in a planetary gearbox derive the vibration signal model of amplitude modulation (AM) and frequency modulation (FM) due to gear damage and determine the characteristic frequencies of vibrations for a planetary gearbox in Sections 3 and 4. The general procedure for the proposed method is shown in Figure 13, and the basic steps are described as follows.  
Step 1: Understand the design parameters and operating conditions of the planetary gearbox, such as the number of gear teeth and input speed.  
Step 2: Calculate the characteristic frequencies using Equations (11)-(18).  
Step 3: Record health and fault vibration signals, which can be represented by time domain waveform signals. Convert time domain waveform signals into frequency domain spectra signals through the fast Fourier transform.  
Step 4: The fault diagnosis in the vibration condition of the faulty gear can be verified by AMFM modulation and experimental results. Characteristic frequencies (\(f_{c}\)) and the rotation frequency of the planet carrier (\(f_{H}\)) directly reflect this mechanical modulation  
Figure 12: Simulated waveform and Fourier spectrum for a signal for planet gear damage, including the effect of rotation of the planet carrier.  
Figure 13: Illustration of the proposed fault diagnosis.  
behavior, especially the sideband around the gear meshing frequency (\(f_{m}\)) and its harmonics. This behavior is consistent with the result using Equation (10).
## 2 Research Background and Significance
### Related Work
#### 2.2.1 Code Auditing with Machine Learning
In the work of [PERSON] et al., they presented a system for Cross Domain Software Vulnerability Discovery (CD-VulD) using deep learning and domain adaptation [19]. They converted the cross-domain program representations into token sequences by AST tools, then used token sequences to build a classifier for vulnerability detection. Experimental results show that CD-VulD outperforms the state-of-the-art vulnerability detection approaches by a wide margin. Their work proved that tokenization is an effective approach and potential research direction in the field of code auditing with machine learning. In the study [20] of [PERSON] et al., they summarized the detailed comparison of indicators in recent researches on source vulnerability auditing using machine learning, as shown in Table 1.  
Due to the universality of XSS threats, using machine learning technology to find XSS vulnerability in source code is a critical research direction. The trigger of XSS often comes from the user input. In XSS-sensitive code blocks, the general code logic is that data from the user input is reviewed or filtered, and finally appears somewhere on the web page [21]. However, there is a problem with both traditional methods and many current machine learning methods: once a filter function used to sanitize the input is detected, the whole code block is considered safe. It ignored the vulnerability caused by an invalid filter. Another extreme situation is that the previously filtering rules set by the coder supposed the use of only one or two cleaning functions is invalid. Then, a large number of filters need to be used to prevent all possible occurrences. In dynamic Web applications, user input may be output in different locations on HTML pages. That means XSS attackers need to construct specific payloads to accomplish the attack. To intercept attacks from different locations, it is critical to choose a corresponding filtering strategy. An effective filter can greatly reduce the possibility of an XSS attack. However, there will be no help at all if several irrelevant filter functions are used to sanitize the input. In previous studies, researchers focused on source code vectorization and machine learning models themselves. However, they ignored the context information [22] in the source code. This dynamic response and combination reveal that for XSS vulnerability detection in the source code, the context information is more vital, especially the relationship between the filter functions and output locations.
## 3 Research Model and Hypotheses
### Hypotheses  
#### 3.2.1 System Quality (SysQ)  
System quality relates to users' perceptions of the performance and effectiveness of an online learning platform. It focuses on aspects such as system reliability, responsiveness, ease of use, and overall functionality [43]. In the domain of distance learning, the satisfaction of learners is notably impacted by the excellence of the system, particularly with regard to factors such as simplicity of use, ease of learning, and user-friendliness [15]. That is, learners are inclined to consistently engage and experience higher levels of satisfaction when the system is user-friendly and navigable. Hence, the first hypothesis was formulated as follows:  
**H1:**: _System quality positively influences student satisfaction_.