esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Data Preparation and Evaluation
```
Input: Electroencephalogram (EEG) data; Output: IAF \(n_{people}\) = 31; \(n_{games}\) = 3; for\(person=0\) to \(n_{people}-1\)do for\(game=0\) to \(n_{games}-1\)do  Load EEG data from file;  eeg_data = \"game[person][game].csv\";  Compute PSD using [PERSON]'s method; [freq, psd] = welch(eeg_data);  Store PSD values in \(psd\_data\);  psd_data[person][game] = psd;  Define alpha frequency band; if(freq \(\geq\) 8) and(freq \(\leq\) 12)then  Store individual alpha frequency (IAF);  iaf_data[person][game] = argmax(psd_data[person][game]) ;  end if  end for  end for  Compute mean IAF values across all persons for each game; iaf_means = mean(iaf_data, axis = 0);  Find game index with highest average concentration according to IAF; best_iaf_game = argmax(mean(iaf_means));
```  
**Algorithm 1**Individual alpha frequency (IAF) algorithm  
To measure FAA, the alpha wave activity was recorded in electrodes in the left and right hemispheres of the brain, and the activity in both hemispheres was compared. The difference in alpha wave activity between the electrodes of the left and right hemispheres was calculated, and this difference was compared with the activity in the alpha band. More significant activity in the left hemisphere than in the right could indicate a concentration state. Algorithm 2 describes calculating the average FAA values for each video game using EEG data. ```
Input: Electroencephalogram (EEG) data; Output: IAF \(n_{people}=31\); \(n_{games}=3\); for\(person=0\)to\(n_{people}-1\)do for\(game=0\)to\(n_{games}-1\)do  Load EEG data from file;  eeg_data = \"game[person][game].csv\";  Compute PSD using [PERSON]'s method; [freq, psd] = welch(eeg_data);  Store PSD values in psd_data;  psd_data[person][game] = psd;  Define alpha frequency band; if(freq\(\geq\)8)and(freq\(\leq\)12)then  Store Frontal Alpha Asymmetry (FAA);  faa_1 = mean (psd_data[1:end/2]);  faa_r = mean(psd_data[end/2 + 1:end]);  faa[person][game] = faa_1 - faa_r;  end if  end for  Compute mean FAA values across all persons for each game;  faa_means = mean(faa_data, axis = 0)  Find game index with highest average concentration according to FAA; best_faa_game = argmax(mean(faa_means))
```  
**Algorithm 2**Frontal alpha asymmetry (FAA) algorithm
# A Numerical Study of the Behavior of Micropile Foundations under Cyclic Thermal Loading
## 3 Results and Discussion
### Mp-30  
In the isolated MP-30 \"inside\" case, the amplitude of pile head movement and thermal stress changes were broadly similar to those for the \"outside\" case, while the averages moved upwards and became less compressive over time, respectively, in a similar way to MP-15. Average pile head thermal movements moved from a small settlement in the first cycle, to a small heave (0.1 mm), while the movement amplitude was about \(\pm\)1 mm. The associated thermal stress amplitudes remained similar to the \"outside\" case and the average thermal stress reduced by about two-thirds but remained in compression.  
In the group unit cell scenario, the response was also broadly similar to the \"outside\" case. A small pile head settlement occurred in the first few cycles which was not matched by pile base settlement, and this differential movement between the pile head and base (elastic shortening) resulted in a locked-in compression stress which later thermal cycles were not able to overcome.  
Irrespective of the pile length and pile spacing (R/D), in cooling, a significant tensile load was generated close to the pile head which largely disappeared during heating cycles. This occurred as a result of the warming of the upper profile under the new surface boundary condition which was antagonistic to the effects generated by pile cooling, i.e., the ground expanding up and the pile contracting down. However, during heating, the effects were complementary as the soil and pile expanded together.  
Again, the generation of tensile thermal stress could be of concern but only if the combined thermo-mechanical stress response became tensile. For example, in this case, the thermal tension generated at the pile head ranged between about 30 and 40 kN, while the initial mechanical load was a compression of 235 kN and 765 kN for MP-15 and MP-30, respectively. Similarly, elsewhere, despite tensile thermal stress developing, the resultant axial stress remained in compression, even at the pile base, e.g., in the MP-30 R/D = 4 case, a thermal tension of about 17 kN was predicted, however, the pile base reaction under mechanical load was 68 kN. This highlights another mitigating effect of close pile spacing in groups which leads to additional load (compression) being transferred to the pile base, prior to any thermal effects being superimposed. As noted previously, micropiles are usually constructed with full-length reinforcement, so even if a resultant tension developed, this would not be detrimental to performance.
## 2 Materials and Methods
### Engineered Features
#### 2.1.1 Local Binary Pattern (LBP)  
Since [PERSON] et al. introduced LBP [19], it has been recognized as a leading technique for texture classification. The standard methodology of LBP sets a threshold for the local neighborhood surrounding each pixel, determined by the central pixel's value. This procedure generates a binary string, considered as a binary code, to which weights are subsequently allocated:  
\[LBP_{N,R}=\sum_{i=1}^{N}2^{i-1}*f(g_{i}-g_{c}) \tag{1}\]  
In this context, \(g_{c}\) denotes the intensity of the central pixel, \(g_{i}\) signifies the intensity values of \(N\) adjacent pixels that are equidistantly distributed on a circumference with radius \(R\) encircling the central pixel, and lastly  
\[f(x)=\begin{cases}1&\text{if }x\geq 0\\ 0&\text{otherwise}\end{cases} \tag{2}\]  
Given that the central pixel's coordinates are set at the origin \((0,0)\), the positions of its \(N\) adjacent pixels, situated on a circular path with a radius \(R\), are determined by the formula \((-Rsin(2\pi i/N),Rcos(2\pi i/N))\). Should these coordinates not align precisely with the pixel grid for obtaining the grayscale value, bilinear interpolation is employed for the necessary estimation. Furthermore, the LBP approach can be adapted for three-dimensional contexts by including neighboring pixels located on a sphere with radius \(R\).  
The main challenge with the traditional LBP is being highly sensitive to noise and distortions in CT scans, which may arise due to various factors including photon count during the examination, slice thickness, and patient size. The reliance on a hard threshold within the conventional LBP framework exacerbates its sensitivity, undermining its robustness against noise. This characteristic renders traditional LBP less effective for applications such as nodule diagnosis, where precision is critical. Potential issues with employing traditional LBP include:  
1. [label=]
2. Nodule Misclassification: Traditional LBP's sensitivity to noise may incorrectly identify benign nodules as malignant, affecting patient treatment and prognosis.
3. Diagnostic Accuracy Decline: Noise and distortions compromise LBP's textural analysis, lowering the reliability of lung nodule diagnosis and potentially delaying appropriate treatment.  
This highlights the necessity for advanced or modified methods capable of reducing noise impact and improving the accuracy of detecting and diagnosing nodules in CT imagery.
# Review of Unmanned Aerial Vehicle Swarm Communication Architectures and Routing Protocols
## 2 Communication Architectures
### Decentralized Communication Architecture
#### 2.2.3 Multi-layer Swarm Ad hoc Network  
\"Multi-layer swarm Ad hoc network\" architecture is another type of architecture that is suitable for a mass variety of UAVs. As shown in Figure 5, this architecture is significantly advanced compared to the \"multi-group swarm Ad hoc network\" architecture. A group of adjacent UAVs of the same type constitutes an Ad hoc network, which is the first layer of the communication architecture. The architecture of intra-group communications is elaborated as explained in Section 2.2.1. Different types of UAV groups rely on gateway UAVs to perform G-T-G communication, which comprises the second layer. The closest gateway UAV communicates with the infrastructure, which is the third layer of the architecture. Communication between any two UAVs in the \"multi-layer swarm Ad hoc network\" architecture does not require infrastructure relay. Mutual communication of UAVs in the same group takes place at the first level. Communication between UAVs in different groups is routed  
Figure 4: Schematic of a multi-group swarm Ad hoc network.  
through the gateway UAV. Data packets go through the first and second layers in turn. Therefore, there is no SPOF in the \"multi-layer swarm Ad hoc network,\" and this type of architecture is robust.  
In a mission, when the number of UAVs changes, the \"multi-layer swarm Ad hoc network architecture compensate for the increase or decrease of UAV nodes and quickly implement network reconstruction. Therefore, the multi-layer swarm Ad hoc network architecture is appropriate for scenarios where UAV swarm missions are complicated: there are a huge number of UAVs executing the mission, the network topology changes, and communication between the UAV nodes is frequent. With the research and development of UAV swarms and the advancement of communication technology, one possible improvement includes the number of layers required to form a swarm Ad hoc network architecture with more UAVs, resulting in larger task coverage and a more robust network [35].
## 6 AOG Downtime Reduction and Implementation Results
### Implementation of Forecasting Approach Performances  
The survey and data analysis provided critical insights into the effectiveness of predictive maintenance systems and their role in optimizing spare part procurement processes. This work emphasizes the value of advanced forecasting methods, such as time series analysis and binary classification, in mitigating operational inefficiencies and reducing AOG events.  
A key component of this study involved implementing Python-based forecasting approaches. The analysis utilized a robust dataset containing 1000 entries derived from airlines' operational and maintenance records spanning various fleet types, including the Airbus A320 and A330 and Boeing 737-800.  
The dataset included the following critical variables:  
AOG_Incident: A binary indicator (0 or 1) of whether an AOG event occurred.  
Date: Timestamp for recorded events.  
Part_Failure_Rate: Component failure rates on a scale from 0.1 to 4.9.  
Figure 10: Accuracy ratings for predictive maintenance of spare parts.  
Maint_Sched_Compliance: Maintenance compliance percentages are used as a proxy for adherence to scheduled checks.  
Total_Flight_Hours: Aggregated flight hours logged.  
Downtime_Hours: Hours of aircraft inactivity due to maintenance or AOG events.  
Delay_Time_Hours: Delays caused by operational or maintenance factors.  
Num_Failures: Count of failures within specific timeframes.  
This dataset was meticulously curated from historical operational metrics and interviews with aviation maintenance experts. Each entry reflects real-world data, capturing the complexity of operational dynamics. The data were further enriched through descriptive and statistical analyses to ensure accuracy and relevance for forecasting model evaluations.
## 3 Experimental Measurements
### Comparison of Simulations and Measurements Results with CPM  
The resulting values for simulations 2 and measurements 2 with CPM are summarized in graphic display in Figure 11.  
Observed differences between the resulting values from measurements and simulations at a distance of 5 mm reach more than 50%. We assume that such a significant  
Figure 11: Summary graphic display of the resulting values for simulations 2 and measurements 2 with an implanted CPM.  
deviation can be caused by several factors, which are listed below. One of the main reasons may be the complexity and number of tissue structures included in the simulated models compared to the phantom used for the experimental measurements. The simulated models contain more detailed representations of human tissues, including bones, muscles, and internal organs, which can affect the EMF interaction and the resulting E values.  
Another significant difference is the actual pacemaker model used in the simulations. To reduce the computational and time requirements, we used a simplified model of a pacemaker, which is hollow or consists only of air, in the simulations.  
The third factor is the distance itself and the characteristics of the near field (up to a distance of several millimetres). The formulas used to convert power levels (E values) are often defined for the far field, which can lead to inaccuracies at minimal distances.  
When comparing the results for larger distances, the deviations between measurements and simulations are significantly reduced: at a distance of 15 mm, the deviations are reduced to less than 30%; at a distance of 50 mm, the deviations are less than 12.33%; and at a distance of 300 mm, the deviations are less than 18.48%.  
The highest deviations for all distances were recorded for the complex model of the child's chest (CHILD). This phenomenon may be due to the different physiology of the tissue structures of children compared to adults, as well as smaller body dimensions, which affect the distribution and interaction of EMF [29; 30].  
The results of our analysis show that the composition of the models, the complexity of the pacemaker, and the near-field characteristics are key factors influencing the differences between experimental measurements and simulations. For more accurate results in simulations, it is necessary to take these factors into account and optimize models and calculation methods, especially at minimal distances. The detected deviations thus raise new questions and emphasize the need for further research in the area of the near field and its impact on implanted medical devices.
## 3 Methods
### The Architecture of the Fusion Network
#### 3.1.4 Loss Function  
To generate high-quality fused images due to GAN features, we added intensity and texture loss functions to the generator loss. So, the generator loss function can be expressed as follows:  
\[L_{\text{generator}}=L_{\text{adversarial}}+\lambda_{1}L_{\text{intensity}}+ \lambda_{2}L_{\text{texture}} \tag{12}\]  
where \(L_{generator}\) denotes the total generator loss function, while \(L_{adversarial}\) and \(L_{content}\) represent the adversarial and content loss functions, respectively. The parameter \(\lambda_{1}\) and \(\lambda_{2}\) is the factor to control the balance between the three terms.  
To make generated images contain more intensity information from infrared and visible light images, we define the intensity loss function as follows:  
\[L_{\text{intensity}}=\frac{1}{HW}(\left\|I_{f}-I_{\text{ir}}\right\|_{2}+ \lambda_{3}\left\|I_{f}-I_{\text{vis}}\right\|_{1}) \tag{13}\]  
where \(I_{f}\), \(I_{ir}\) and \(I_{vis}\) represent the fusion image, infrared image, and visible image. \(\left\|\cdot\right\|_{1}\) and \(\left\|\cdot\right\|_{2}\) denote the L_1-norm and L_2-norm, respectively. The parameter \(\lambda_{3}\) is a weighted coefficient.  
To make the fusion image retain more texture information, we introduce texture loss to assist intensity loss. The texture loss is shown as follows:  
\[L_{\text{texture}}=\frac{1}{HW}(\left\|\left|\
abla I_{f}\right|-\text{max}( |\
abla I_{ir}|,|\
abla I_{vis}|)\right\|_{1}) \tag{14}\]  
where \(\
abla\) represents the gradient operator.  
In DAGANFuse, the adversarial loss consists of two parts: the adversarial loss between the generator and each of the two discriminators, \(D_{ir}\) and \(D_{vis}\). The adversarial loss is formulated as follows:  
\[L_{\text{adversarial}}=-\mathbb{E}\left[D_{ir}(I_{f})\right]-\mathbb{E}\left[D _{vis}(I_{f})\right] \tag{15}\]  
In addition, we define the loss functions of the two discriminators based on the Wasserstein distance:  
\[L_{D_{ir}/D_{vis}}=\mathbb{E}\left[D_{ir/vis}(I_{f})+\lambda_{4}\left(\left\| \
abla D_{ir/vis}(\widetilde{I}_{ir/vis})\right\|_{2}-1\right)^{2}\right] \tag{16}\]  
Figure 4: The architecture of the discriminators: \(3\times 3\), filter size; Conv, convolutional layer; FC, fully connected layer.  
In the formula, the first part represents the Wasserstein distance estimation, and the second part is the gradient penalty. \(\lambda_{4}\) is defined as the regularization parameter.
## 2 Related Works  
This section reviews and discusses the network protocols and related works for massive deployment. At least four types of communication protocols, including the unicast, broadcast, multicast [39], and P2P networking [40] can be used for massive deployment. Normally, the unicast protocol is not suitable for massive deployment due to the limitations of scaling up. The availability network bandwidth for each client machine decreases linearly as the number of client machines increases when a unicast protocol is used. The broadcast, multicast, and P2P protocols have specific advantages and disadvantages when applied to massive deployment. In the following sub-sections, the works related to unicast, broadcast, multicast, and P2P mechanisms are addressed.
## 2 UTM Monitoring Framework
### Conflict Detection Algorithm
Second, when an intruder UAV is at a distance between 3r and 6r from the detecting UAV, it will trigger a warning if the intruder location is inside the area of the inverted teardrop shape. Previously, the relative direction angle \(\theta\) and distance d between two UAVs were calculated using Equations (2) and (5). Then, angle \(\delta\) between the UAV's relative direction to the flight direction of UAV 1 is calculated using Equation (6):  
Figure 1:0  
When angle \(\delta\) is between \(-60^{\circ}\) and \(+60^{\circ}\), UAV 2 could be inside the teardrop area. Then, the reference distance of the teardrop boundary b for the angle \(\delta\) is calculated using Equation (7):  
Figure 1:1  
Equation (7) shows that the closer the intruder UAV is to the flight path of the detecting UAV, the longer the detection distance is considered. This is the difference between this teardrop shape area detection algorithm compared with the circle detection area. If the distance between UAVs d is less than the reference distance b, it means that the other UAV is inside the detection area from a two-dimensional perspective. However, the height must be considered to confirm it as a three-dimensional perspective. Then, a warning of conflict detection will be produced; otherwise, no warning will be produced. Another possibility is when the distance between UAVs d is less than or equal to 3r and more than r; then, a warning detection will be produced after the height is also considered. This means that the nearby UAV is in the circle part of the teardrop detection volume. The last possibility is the distance is less than equal to r and the height difference is less than or equal to h, which means that the nearby UAV is inside the near collision volume. Then, a warning for a near collision will be produced.
# Excavation and Construction Technology of Diversion Tunnel under Complex Geological Conditions
## 10 Discussion and Summary  
Based on the above analysis, it can be concluded that the diversion tunnel excavation construction technology designed in this article has achieved good application results. Combined with specific technical content and experimental results, the innovative breakthroughs of this technology are summarized as follows:  
1. Introducing TSP technology to achieve advanced geological prediction. By using TSP technology, advanced geological prediction can be made for excavation projects of diversion tunnels under complex geological conditions, identifying potential geological problems and difficulties in advance and providing a basis for decision making during the construction process.
2. Combining detection technology for data collection utilizing detection technology to collect two-way travel time, amplitude, and waveform data. By processing and analyzing the detection image, the spatial orientation and length of the main tunnel during the construction of the diversion tunnel are calculated, providing support for the accuracy and accuracy of construction.
3. Designing multiple excavation and support technologies in a targeted manner. Due to the differences in geological conditions and tunnel sections in terms of geological structure, stress state, excavation sequence, etc., targeted designs have been made for tunnel curtain excavation technology, tunnel body excavation and support technology, excavation grouting technology, excavation technology for unfavorable geological tunnel sections at the entrance, upper/lower flat section excavation technology, upper/lower curved section excavation technology, vertical shaft section excavation  
Figure 9: The resistance coefficient of side walls in the construction of water diversion tunnel.  
technology, etc. to adapt to the complex and ever-changing underground environment and ensure the safety of diversion tunnel construction as well as stability and quality. The excavation grouting technology can enhance the bearing capacity and stability of the soil around the tunnel body, further improving the antidamage ability of the side wall.
* Establishing a construction quality control system. During the tunnel construction process, a construction quality control system is established to monitor and manage all aspects of the construction, ensuring that the construction quality meets the requirements.
* Planning for the reuse of slag materials. The reuse process of slag materials is planned to minimize resource waste and improve the sustainability and economic benefits of the project.  
These innovative points enable the excavation technology of the diversion tunnel to have better geological prediction ability, accurate data support, diversified construction technology selection, construction quality control, and reasonable utilization of resources under complex geological conditions, thereby improving the efficiency and safety of excavation construction.
# Joint Timekeeping of Navigation Satellite Constellation with Inter-Satellite Links
## 4 Performance Evaluation of ISLT
### Predictability Improvement  
The prediction accuracy of satellite clocks is important for positioning and timing performance. In this part, we evaluate the predictability of the ISLT. For the short-term prediction, a linear model was used to fit the prediction parameters by using the MPOD clock deviations that were collected every two hours and then used to evaluate the predictability over the next two hours. The prediction accuracy was assessed with the root-mean-square error (RMSE) and is displayed Table 4. The optimal short-term prediction accuracy of the PHM was 0.27 ns, as in [3]. Except for C19 and C26, the short-term prediction accuracy of PHMs was generally superior to the Rb clocks. The prediction accuracy of the ISLT was 0.18 ns better than all the timekeeping clocks.  
For the long-term prediction, a linear model was used to fit the prediction parameters by using the MPOD clock deviations that were collected every 24 h and then used to evaluate the predictability over the next 10 h. The prediction accuracy is displayed in Table 5. The optimal long-term prediction accuracy of the PHM was 1.16 ns worse than the result in [3]. The reason for this is that the MPOD clock deviation absorbed the orbit error, while the TWSTFT clock deviation did not. Except for C26, the long-term prediction accuracy of PHMs was generally superior than all the Rb clocks. The prediction accuracy of the ISLT was 1.05 ns better than all the timekeeping clocks.