esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine Learning-Based Methods for Enhancement of UAV-NOMA and D2D Cooperative Networks
## 3 UFBS Placement Procedure
### 3D UFBS Placement  
Following the determination of the UFBS's 2D deployment location, the 3D UFBS placement procedure adjusts the UFBS's altitude to provide the highest quality of service to GMTs within the area of interest \(A\). Thus, the farthest GMT\({}_{l}\) from the point \(p_{1}\) where the UFBS is finally placed should be identified, according to the horizontal two-dimensional distance \(r_{l}\). After that, the convenient height for the critical point \(p_{1}\) is found by solving the following equation using (1):  
\[\frac{\partial\text{P}\Gamma_{l}^{u}(h,r_{l}^{P_{1}})}{\partial h}=0. \tag{30}\]  
For the considered A2G path-loss model, as the altitude of the UFBS increases the path loss initially decreases and then increases again. This behavior can be attributed to the dependence of the particular A2G model on the elevation angle and the distance between the UFBS and each GMT\({}_{l}\). As the height of the UFBS increases the elevation angle also increases, leading to an increased probability of line-of-sight, i.e., obscurance by buildings and other surrounding objects is reduced. Based on this behavior, the A2G path loss PL\({}_{l}^{u}\) function is convex [25]. Thus, it can be deduced that the global minimum is consistently located at the critical point which can be derived through the Equation (30).  
```
1:input: The set of coordinates of all GMTs \(U=\{u_{1},u_{2},\ldots,u_{N}\}\), and the number of UFBSs Y
2:\(\epsilon=10^{-6}\)
3:\(t=0\)
4: Initialize Y medoid points \(C^{t}=\{p_{1}^{m},p_{2}^{m},...,p_{Y}^{m}\}\subseteq U\), randomly
5:\(S_{k}=\emptyset,\forall_{k}=1,2,\cdots,Y\).
6:for\(i\gets 1\) to \(N\)do
7:\(k^{*}=\underset{k=1\ldots Y}{\operatorname*{arg\,min}}\|u_{i}-C_{k}^{t}\|\)
8:\(S_{k^{*}}=S_{k^{*}}\cup\{u_{i}\}\)
9:endfor
10:\(A^{t}=\sum_{k=1}^{Y}\sum_{u_{i}\in S_{k}}\|u_{i}-C_{k}^{t}\|^{2}\)
11:repeat
12:for\(k\gets 1\) to Y do
13:for\(i\gets 1\) to \(N\)do
14:if\(u_{i}\
otin C^{t}\)then
15: Swap the role of \(C_{k}^{t}\) with \(u_{i}\)
16: Repeat steps 6 to 9
17:\(B=\sum_{k=1}^{Y}\sum_{u_{i}\in S_{k}}\|u_{i}-C_{k}^{t}\|^{2}\)
18:if\(B<A^{(t}\|\)then
19:\(t=t+1\)
20:\(C_{k}^{t}=u_{i}\)
21:\(A^{t}=B\)
22:endif
23:endif
24:endfor
25:endfor
26:until\(C^{t}-C^{t-1}\leq\epsilon\)
27:output: A set of centroid points that the Y UFBSs will be deployed \(C^{t}\).
```  
**Algorithm 2** 2D UFBS placement process through the k-medoids algorithm
# Modeling and Assessment of GPS/BDS Combined Precise Point Positioning
## 4 Parameter Differences between Combined PPP Models
### Kinematic Coordinate Difference  
As discussed above, in the simplified GPS/BDS PPP model where ISB is not considered, the coordinate solution is indentical with that of traditional model. Here the coordinate difference between traditional and new model of JFNG's kinematic solution is presented in Figure 7. After 8 epochs the coordinate differences is less than 1 cm, and after 30 epochs the difference reduces to less than 1 mm. After 500 epochs the difference is less than 5 \(\upmu\)m and remains stable until the last epoch.
# Health Monitoring of Civil Infrastructures by Subspace System Identification Method:
## Vibration-Based Damage Detection (VDD)  
VDD is considered to be the most popular methodology in global SHM. VDD methods rely on changes in dynamic properties as an indicator of damage existence. These methods exploit observable  
Figure 1: Classification of the structural health monitoring (SHM) methods with focus on subspace system identification (SSI) algorithm.  
variations in modal parameters, such as resonant frequency, damping, and mode shape or their derivative as indicators of change in physical properties of a structure. A thorough review of the system identification methods in VDD and modal analysis is provided in [PERSON] et al. [42] and [PERSON] [43].
# Prediction of Blade Tip Timing Sensor Waveforms Based on Radial Basis Function Neural Network
## 2 Static Calibration of Sensor Waveforms  
When measuring the BTT data of a given thickness of the blade using a given tip timing sensor, the OV of the BTT sensor is mainly affected by the radial distance \(x\) between the blade tip and the sensor, and the angle \(\alpha\) between the centerline of the blade and the sensor. Hence, the output voltage can be shown by the following formula:  
\[U=U(x,\alpha) \tag{1}\]First, calibrate the waveforms of the BTT sensor under static conditions. The experimental data between \(U-x\) and \(U-\alpha\) are measured on the static calibration experimental bench as shown in Figure 1. Its principle is to fix a value of blade tip radial clearance \(x\), and then measure the voltage \(U\) at different angles \(\alpha\). Thus, the \(U-\alpha\) curves at different clearances are measured. Lastly, within the radial clearance value measurement range (0.1-0.55 mm), the eddy current sensor measures the output voltage values for each angle within the different radial gaps under the conditions of this signal. After fitting the data, the calibrated sensor output waveform set is obtained.  
A dynamic calibration system mainly involves a radial and circumferential calibration system. The radial calibration system consists of manual fine-tuning X-Y direction displacement sliding platform, a sensor and its fixed bracket. During the radial calibration, the Y-directional sliding platform is adjusted to keep both the blade centerline and the BTT sensor centerline coincident to the extent possible. Subsequently, the X-directional sliding platform is adjusted so that the transducer bracket fixed on the displacement table moves slightly with the transducer in the X-direction, thus adjusting the radial distance between the transducer probe and the bladed tip. A peripheral calibration system is composed of a manual fine-tuning rotary table in the R-direction, a fixed bracket, a peripheral adjustment plate and other components. Among them, the fixed bracket is connected to the operating platform by a fixing hole in it. The rotating table is bolted to the fixed bracket, on which the circumferential adjustment plate is fixed. A circumferential adjustment plate is provided with radial blade mounting slots to obtain different blade tip radii. To perform the circumferential calibration, the rotary table is first set to coarse adjustment and rotated so that the blade is in the centered position with the sensor. Afterwards, switch the rotary table to fine adjustment, and rotate the R-direction micrometer tip to revolve the circulating adjustment plate, so as to push the blade to rotate slightly and accomplish the circumferential calibration.  
At a constant value of the BTC, there is an output voltage of the sensor which can be summarized as follows:  
\[U=f(x) \tag{2}\]  
In which \(x\) is the peripheral displacement of the blade tip relative to the speed key phase sensor, \(f\) is a function of the fitted stationary calibration curve.
# Power Efficient Random Access for Massive NB-IoT Connectivity
## 1 Introduction
Thus, after the configured number of reattempts in the initially selected CE level, the RAP reattempts are performed in the higher CE levels [9]. The number of repetitions and the power level are high in the higher CE level. Moreover, the RAP reattempts can also be performed by power ramping power, i.e., by increasing the transmission power (3 GPP defines parameters (_powerRampingStep_' and optionally _powerRampingStepCE1_')) [8; 10]. In a massively connected NB-IoT environment where collisions are suppteen, the power ramping and sequential CE level upgradations would sharply degrade the performance of RAP in terms of power consumption. While it is admissible to increase power due to poor channel conditions, power ramping  
Figure 1: Coverage enhancement levels and repetitions. may not be necessary when failures occur due to collisions. Thus motivated, we propose a novel power efficient RAP (PE-RAP) for NB-IoT devices in congested environments. In PE-RAP, the devices do not indiscriminately perform power ramping nor do they unnecessarily move to the higher CE level for RAP reattempts. The devices have flexibility to re-select the CE level after failure in PE-RAP such that the channel conditions are re-ascertained at every RAP reattempt. More precisely, our contributions are as follows:  
1. In PE-RAP, the devices reaffirm the channel conditions. Subsequently, the appropriate power level and repetitions are re-selected such that chance of failure due to poor channel are reduced. Higher CE levels are configured for transmission at higher power and thus power ramping becomes implicit. 2. Every RAP reattempt causing more power consumption can occur only after the collisions in PE-RAP. Thus, we also evaluate the collision probability of the devices. 3. The access class barring (ACB) mechanism is popular for congestion control in machine type communications. ACB probabilistically controls the preamble transmissions by the devices. We incorporate the ACB mechanism in our proposal since high connectivity is considered. 4. We analyze the average power consumption in the proposed PE-RAP and compare it to the existing NB-IoT RAP. Additionally, extensive simulations are carried out to validate the analytical results. The rest of the paper is organized as follow. The NB-IoT physical random access channel (NPRACH) fundamentals and related work are delineated in Section 2 to highlight the effect of repetitions and collisions on NB-IoT devices. Section 3 presents the details of the proposed PE-RAP mechanism and its analytical modeling. The mathematical model for power consumption in the existing NB-IoT RAP is derived in Section 4. In Section 5, the numerical and simulation results are presented. Finally, Section 6 summarizes the conclusion.
# Deep Analysis of Student Body Activities to Detect Engagement State in E-Learning Sessions
## 7 Experimentation and Evaluation
### Experiment 4  
In this experiment, we evaluated our proposed models, PM2 and PM3, with an unseen dataset. For this experiment, we explored the available options, which were (1) collecting new data from volunteers or (2) using a public dataset. The first option was not available because we could not find new volunteers as students returned to study in person after the lockdown was over. Therefore, we explored the publicly available datasets and found that the DAiSEE [15] shared aspects with our dataset. For example, it was recorded in uncontrolled settings. However, we emphasize that the majority of the DAiSEE dataset is not 100% suitable for our requirements, for several reasons. For example, the volunteers were given specific scenarios, but they did not attend real online classes. It also focuses on the volunteers' faces because it uses facial expressions for engagement detection. Nonetheless, the students' bodies were clear in a large number of video clips, which we selected for Experiment 4. The sessions were recorded for a long duration and trimmed to about 5-10 s clips, which is similar to our data. The authors reported the accuracy, which ranged between 36.43% and 39.8%, using three classifiers (KNN, SVM, and random forests (RF)). The creators of the DAiSEE dataset stated that the obtained results showed the difficulty of working with varying user positions, poses, illumination settings, and background noise. The DAiSEE contains four affective states (engagement, frustration, confusion, and boredom). For this experiment, we discarded many videos. For example, when the volunteers' bodies were not clear or the clip included more than one student (see Figure 10).  
Figure 10: Example of some of the discarded scenarios from the DAiSEE dataset [15].  
Figure 9: Comparison with state-of-the-art engagement detection methods [5; 7; 10; 11; 27; 28; 29].  
We selected 30 videos that were similar to our collected videos and requirements and evaluated our prediction models using them (see Figure 11). The used videos include both female and male students. For our purpose, we classified the videos using the same approach followed during our dataset annotation phase, while taking into account the affective model in [8]; for example, videos of the DAiSEE in the engagement state were classified as high positive engagement. Frustration and boredom were classified as low positive engagement and confusion as low negative engagement. As for the disengagement class, we considered any video with the student eating, being quiet/still, talking to someone else, etc. as belonging to this class. Figure 12 shows the obtained results. As can be seen in Figure 12, PM2 and PM3 perform very well with unseen data.
# Static and Dynamic Response of Micropiles Used for Reinforcing Slopes
## 3 Results
### Displacement Response Analysis  
The displacement variations of D1, D3, and D5 are in Figure 11. It can be seen from the figure that the settlement of D1 (inside of platform) gradually increases with the load, but the rate of the settlement deformation speed is relatively constant, with no sudden change. At the end of the loading, the maximum vertical deformation is approximately 20 mm. Additionally, the deformation at the D3 measuring point (located on the top of the #2 pile in the front row) is more consistent with that of measuring point D5 (located at the foot of the front edge of the landslide mass). Prior to the load of 22 kPa, the displacement of D3 and D5 change slowly with the accumulation of the load, however, the micropiles gradually fail as the load increases. The landslide mass had a large degree of sliding deformation. At the end of loading, the final displacement of D3 and D5 are 80.23 mm and 87.12 mm, respectively. It can be inferred from the test results that a critical load exists. Beyond it, the displacement of the pile top and the slope toe will increase quickly. At this critical load, it was determined that the micropiles have completely entered the plastic deformation stage. In this paper, this load is called the critical failure load. In the static model test, the critical failure load of the model is around 22 kPa.  
Figure 11: Displacement change view of D1, D3, and D5.  
By comparing Figure 9a,b, it can be seen that after the shake table test, the displacement of the landslide model under the dynamic loading (slope toe, slope top, platform) is greater than the static, which illustrates that dynamic loading causes more damage to the piles than the static loading case.
## 5 Efficiency
### Implementation
Since the current network delay for each hop is 20 ms and the signature generation by each smart meter requires 5 ms, the total time overhead would be \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Operation** & **Time (ms)** & **Smart Meter** & **Concentrator** \\ \hline Pairing & 2.9 & - & 4 \\ Exp. in \(\mathbb{G}\) & 1.0 & 5 & - \\ Exp. in \(\mathbb{G}_{T}\) & 0.2 & - & \(N\) \\ Computation (ms) & \(5.0\) & \(0.2N+11.6\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Computation cost for signing and verification. at most \((20+5)*(\text{log}(N+2)-1)+(0.2N+11.6)\) ms. Thus, as long as the metering frequency is less than \(1/(25*(\text{log}(N+2)-1)+(0.2N+11.6))\) ms, the overhead of the proposed security mechanism would be tolerable in a smart grid network.
## 2 Methodology  
### Attention Module  
Attention mechanisms have become an increasingly popular technique in deep learning for a wide range of tasks, including image recognition, speech recognition, machine translation, and more. Attention mechanisms allow the model to focus on the most important features or regions of the input, rather than processing the entire input at once. This can significantly improve the accuracy and efficiency of the model [21; 22].  
The basic idea behind attention mechanisms is to assign weights to different parts of the input, based on their relevance to the task at hand. For example, in image recognition, the model might assign higher weights to the most important parts of the image, such as the object of interest or the background context. By doing so, the model can selectively attend to the most informative parts of the image, while ignoring the irrelevant or noisy parts [22; 23].  
One of the main benefits of attention mechanisms is their ability to handle variable-length inputs. Unlike traditional models, which require fixed-length inputs, attention mechanisms can process inputs of any length, by dynamically adjusting the weights assigned to different parts of the input. This makes attention mechanisms particularly well-suited for tasks such as machine translation, where the length of the input and output sequences can vary greatly [24; 25].  
Another benefit of attention mechanisms is their interpretability. By visualizing the attention weights assigned to different parts of the input, we can gain insights into how the model is making its predictions. This can be useful for debugging and improving the model, as well as for generating explanations for the model's outputs [26; 27].  
In our work on breast tumor segmentation, we used a spatial attention mechanism to highlight the most important features of the input image. Spatial attention mechanisms operate on the spatial dimensions of the input, assigning higher weights to the most important spatial locations. This can be useful for tasks such as object localization or segmentation, where the model needs to identify the location of the object of interest within the input image [23; 28].  
The spatial attention mechanism we used in our work has several advantages. First, it can improve the accuracy of the segmentation results, by highlighting the most important features of the input. Second, it can reduce the impact of noisy or irrelevant features, by selectively amplifying or suppressing the features based on their importance. Third, it can speed up the training and inference of the model, by reducing the amount of information the model needs to process. Finally, it can provide insights into how the model is making its predictions, by visualizing the attention weights assigned to different parts of the input. The suggested spatial attention mechanism in this study is demonstrated in Figure 1.
## 4 Numerical Results
### Region of Stability for Different Coin Sizes
The value of \(\varepsilon\) deceases between 3 and 6, and remains approximately the same thereafter (\(\varepsilon\simeq\pi/4\)) as can be seen in Figure 6. The behavior of \(\varepsilon\) is slightly different when we evaluate it for different percentage of the maximum probability. Two examples for \(0.9\times p_{max}\) and \(0.7\times p_{max}\) are shown in Figure 6.  
Based on Monte Carlo simulations, we predict that \(\varepsilon\), when \(p(\phi\in(\phi_{max}-\varepsilon_{-},\phi_{max}+\varepsilon_{+}))\simeq 0.9 \times p_{max}\), and for coin constructed according to Equations (17) and (16), will decrease with increasing the coin size and for large coin \(\varepsilon\longrightarrow 0\). However, when using Equation (18) with \(\alpha=\alpha_{ML}\), \(\varepsilon\) will remain constant with increasing the coin size. The interval \(\varepsilon\) with Equation (18) and \(\alpha=(-1/(2\pi))\), will become closer to \(\varepsilon\) corresponding to \(\alpha=\alpha_{ML}\) and for large coins \(\alpha_{ML}\longrightarrow(-1/(2\pi))\). For lower percentage of the maximum height (for example \(0.7\times p_{max}\)), for all those functions \(\varepsilon\) will decrease slowly with increasing the coin size and for large coin they will converge to a fixed value. The observations given above are supported by ML simulations, when we consider some drawbacks of our ML models, explained in Appendix F.  
The analysis above gives us the base to assert that the half-width \(\varepsilon\), did not decrease with increasing the coin register size if Equation (18) with optimal parameters is used. This indicates that such modification of QRWS algorithm retains its high robustness even for large coin size. Furthermore, that becomes very important when the registers reach several qudits enough for solving practical problems.
## 3 The Proposed Model
### Security and Group Key Management Center  
The security manager coordinates with a cluster-based secure routing method by decrypting the information at the receiver end and encrypting the information at the transmission side [26]. Now, the key is produced by combining the SCA- and FHE-based key generation system to provide secure transmission. By using the root agents, a novel specialized agent has been generated to manage security activities. The security agent employs the rule and makes the decision to select the proper encryption technique to secure the information before it is transmitted. Its interaction with the trust management model exists from the security manager to compute the privacy level of the node. Through the neighbor feedback-based trust values, historical trust, and current behavioral trust, the trust manager calculated the entire trust. When any data transmission node attempts to decrypt the information, the trust management model monitors them. Lastly, the security manager interacts with the group key management and distribution center for re-keying, key generation, and key distribution. The group key management center is accountable for key generation, re-keying, and key distribution activities owing to the existence of change in the group size and group members. Once the gathered information is only decrypted, it forms extremely secure groups with 2 members, such as the source and target. Then, multicast transmission will be implemented once the information is utilized by the intermediate node for data analysis or aggregation. The multicast group key was produced and only distributed securely by the key distribution center to consider the group member. Similarly, due to the existence of member leave and member join operations, it is accountable to maintain the forward and backward privacies that are required. The group key manager constructed by the root agent is accountable for making communication within these components and all other components, such as the routing and security managers.