esa-sceva/satcom-chunk-collection huggingface dataset:
# Communication Interface Manager for Improving Performance of Heterogeneous UAV Networks
## 2 Related Works
### Homogeneous Systems
The proposed model is based on a cost function for multiple UAV deployment, using user demand patterns to assign a cost and density function to each area and UAVs. The authors used a reverse neural model based on user-demand patterns to match each UAV to a particular demand zone. The goal was to provide continuous data between macro cells and UEs, using the UAVs to enhance load balancing by forming multiple intermediate links between the macro cell and the small cell UEs.The results showed that using UAVs yielded 37.7% fewer delays in comparison with a network comprising small and macro cells without UAVs. Regarding altitude variation, it was shown that increasing the altitude provides less interference and appropriate LOS, but also introduces more delays. The probability of guaranteeing the SINR for a particular user in a macro cell was closer to one in both cases using the UAV, showing high connectivity during the connectivity time in comparison with existing ground-based wireless networks. Lastly, an emerging solution relates to the use of satellite-based augmentation systems (SBAS), which promote the integration of UAVs in the National Airspace System (NAS). It allows UAVs to operate harmoniously, close to each other, in conjunction with manned aircraft, occupying the same airspace and using the same air traffic management (ATM) systems and procedures [24]. Thereby, performance-based navigation (PBN) can be applied to UAVs to operate similarly to aircraft that rely on satellite positioning. The base idea is to use the existing message exchange structure from PBN to UAVs, through reference stations using its global navigation satellite system (GNSS) to define which stations are used for communication establishment. SBAS was originally designed and developed for civilian aviation, but now it will be probably possible to also use SBASs in non-aviation applications. Some studies reported good performance in terms of battery consumption, extending its flight time, and improvement in its navigation performance without additional communication channels and receipt of correction messages from a ground control system [25; 26].
# Towards Bio-Inspired Anomaly Detection Using the Cursory Dendritic Cell Algorithm
## 5 Validation and Results
### SKAB Results  
The authors of \(S\!K\!A\!B\) also made available a scoreboard, i.e., a public score table that includes results of different techniques used for anomaly detection, and it uses as scoring metrics: \(F_{1}\) score, False Alarm Rate (FAR), and Missed Alarm Rate (MAR). Thus, by using the metrics that are suggested in \(S\!K\!A\!B\), a comparison can be made between CDCA and other anomaly detection algorithms. Table 1 shows a comparison, using the metrics mentioned, between CDCA (with different parameter combinations) and existing algorithms validated in \(S\!K\!A\!B\).  
The nomenclature CDCA_N_S_k is used, which corresponds to CDCA using \(N\) cells, from which \(S\) is sampled for each data point and \(k\) clusters for K-Means. Furthermore, \"_original_\" denotes that no extra features (moving average and moving average of derivative) were added. Only the original features are used.  
CDCA shows competitive results with most algorithms currently in the \(S\!K\!A\!B\) table. Its \(F_{1}\) score is 0.07 below the highest contender--a convolutional autoencoder--and 0.01 below the second best. The \(F_{1}\) score for CDCA is reasonably similar for multiple parameter variations, never changing more than 0.01. Using K-Means alone, without CDCA, leads to a decrease in \(F_{1}\) score of 0.11. Using no additional features results in a decrease in the \(F_{1}\) score of 0.01, concerning CDCA with the moving average and moving average of derivative. Regarding FAR, CDCA is consistently worse (higher value) than all other current implementations, with MAR values being consistently better (lower value). As CDCA has  
Figure 10: _Sklotech Anomaly Benchmark (\(S\!K\!A\!B\))_ testbed. Figure obtained from [44]. 1,2—solenoid valve; 3—a tank with water; 4—a water pump; 5—emergency stop button; 6—electric motor; 7—inverter; 8—compactRIO; 9—a mechanical level for shaft misalignment.  
the limitation that classification is, in the majority of the cases, not immediately possible, the cycle delays and timing delays are also presented in Table 2. Cycle delays correspond to how many additional data points were needed to be received before classification could be achieved.
## 2 Analysis of the Pure Random Orthogonal Search (PROS) Algorithm
### One-Dimensional Functions  
In this section, we study the performance of PROS on one-dimensional functions defined on the domain \(\Omega=\big{[}a,b\big{]}\). Since we are interested in the case where \(\epsilon\) is small, we assume that \(\frac{2\epsilon}{L}<b-a\).  
For any \(\epsilon>0\), the region of the global minimum contains an interval of minimum length \(\frac{\epsilon}{L}\) for all one-dimensional functions.  
Proof.: Let \(\mathbf{x}^{*}=(x_{1}^{*})\) be the global optimum, \(\mathbf{x}^{l}=(x_{1}^{l})\in R_{\epsilon}\) be a point on the left of \(\mathbf{x}^{*}\) and \(\mathbf{x}^{r}=(x_{1}^{r})\in R_{\epsilon}\) be a point on the right of \(\mathbf{x}^{*}\). We are going to find a sufficient condition to ensure that \([x_{1}^{l},x_{1}^{r}]\) is in the region of global optimum. Since \(f\) is a Lipschitz function, the interval \([x_{1}^{l},x_{1}^{*}]\) belongs to \(R_{\epsilon}\) if \(x_{1}^{l}\) satisfies  
\[f(\mathbf{x}^{l})-f(\mathbf{x}^{*})\leq L\|\mathbf{x}^{l}-\mathbf{x}^{*}\|=L( x_{1}^{*}-x_{1}^{l})\leq\epsilon,\]  
or equivalently, \(x_{1}^{l}\geq x_{1}^{*}-\frac{\epsilon}{L}\). We want to make \(x_{1}^{l}\) as small as possible, so we let  
\[x_{1}^{l}=\max\{a,x_{1}^{*}-\frac{\epsilon}{L}\}.\]  
Similarly, the interval \([x_{1}^{*},x_{1}^{r}]\) belongs to \(R_{\epsilon}\) if  
\[x_{1}^{r}=\min\{b,x_{1}^{*}+\frac{\epsilon}{L}\}.\]  
Since we have assumed \(\frac{2\epsilon}{L}<b-a\), it is impossible that \(x_{1}^{l}=a\) and \(x_{1}^{r}=b\) hold simultaneously. Therefore, at least one of the intervals has length \(\frac{\epsilon}{L}\). Hence, the length of the region of global optimum is at least \(\frac{\epsilon}{L}\).  
The expected convergence time of PROS for one-dimensional functions is bounded above by \(\frac{L(b-a)}{\epsilon}\).  
Proof.: Let \(l\) be the length of the region of global optimum, \(R_{\epsilon}\). The probability that a point chosen uniformly at random falls in \(R_{\epsilon}\) is given by  
\[p=\frac{l}{b-a}\geq\frac{\epsilon}{L(b-a)}.\]  
The convergence time \(T\) is geometrically distributed with parameter \(p\). Its expected value is given by  
\[\mathbb{E}[T]=\frac{1}{p}\leq\frac{L(b-a)}{\epsilon}.\]  
Proos converges in probability to the region of global optimum for all one-dimensional functions, i.e.,  
\[\lim_{t\to\infty}P\{\mathbf{x}^{(t)}\in R_{\epsilon}\}=1,\ \ \forall\epsilon>0.\]  
Proof.: For any realization \(\{\mathbf{x}^{(t)}:t=0,1,2,\ldots\}\), \(f(\mathbf{x}^{(t)})\) is monotonically non-increasing in \(t\). If \(\mathbf{x}^{(t)}\in R_{\epsilon}\), then \(\mathbf{x}^{(t+\tau)}\in R_{\epsilon}\) for all non-negative integer \(\tau\). Therefore, \(P\{\mathbf{x}^{(t)}\in R_{\epsilon}\}\) is monotonically non-decreasing in \(t\). Since the sequence is bounded above by \(1\), it is convergent.  
Given any \(\epsilon>0\), the expected convergence time is bounded according to Theorem 3. If \(\lim_{t\to\infty}P\{\mathbf{x}^{(t)}\
ot\in R_{\epsilon}\}\) was non-zero, then the expected convergence time would be unbounded, which leads to a contradiction.
## 3 Measurement Setup  
We have measured six SMPS for 60 min with our test bench and evaluated their THD\({}_{i}\) and current harmonics. The test bench involves a personal computer (PC), a 4 kVA controllable power supply Chroma 61505 (Chroma system solutions, USA), a data acquisition (DAQ) module (National Instrumentation, United States), controllable electronic DC load TENMA 72-13210 (Premier Farnell, United Kingdom) and power quality measurement device a-eberle PQ-Box 200 (A-Eberle, Germany). The PQ-Box 200 can record at 200 ms resolution according to the standards IEC 61000-4-30 for CLASS A. These 200 ms data points are aggregated to 1-s interval data available to extract from the PQ-Box 200. Relays are used to switch all SMPS automatically. A control box is designed to provide 12V DC to the relays. The 50 Hz reference waveform for the power supply is generated with a sampling frequency of 100 kHz using MATLAB program through the DAQ module. The DAQ module is capable of generating an analog signal corresponding to the digital input. This reference signal (\(V_{rf}\)) has enabled us to generate a pure sinusoidal voltage through the programmable power supply as defined by Equation (1).  
\[V_{rf}=\frac{V_{o}}{V_{range}}\times V_{co} \tag{1}\]  
Here, \(V_{range}\) is 300 V and \(V_{co}\) is 7.072. The digital inputs to switch the relays are also generated using the same the MATLAB program via the DAQ module. The reference signal is calculated by using the relation indicated by Equation (2).  
\[V_{out}=\sum_{i=1}^{n}\sqrt{2}\,\times A_{i}\,\sin(2\pi f_{i}t_{s}+\alpha_{i}) \tag{2}\]  
\(A_{i}\) and \(\alpha_{i}\) are the root mean square values of the harmonics and its phase difference from the fundamental frequency, respectively. The harmonic frequency is shown by \(f_{i}\) and sampling interval by \(t_{s}\). The number of samples needed for the specific duration (\(T_{m}\)) of the voltage output from the controllable power supply can be calculated by Equation (3).  
\[n=T_{m}\times f_{s} \tag{3}\]  
Here \(f_{s}\) is the sampling frequency and its value is 100 kHz. The controllable DC electronic load is used to operate each SMPS with 60% of its rated capacity. Figure 3 shows the block diagram of our test setup.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Harmonic Order (h)**} & **Maximum Permissible** & **Maximum Current** \\  & **Harmonic Current per Watt (mA/W)** & **Harmonic Limit (A)** \\ \hline
3 & 3.4 & 2.30 \\
5 & 1.9 & 1.14 \\
7 & 1 & 0.77 \\
9 & 0.5 & 0.40 \\
11 & 0.35 & 0.33 \\ \(13\leq\text{h}\leq 39\) & 3.85/h & 0.15(15/h) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Current harmonic limits for class D devices.
## 3 Results
### Hyperparameter Optimization
#### 3.2.3 Learning Rate Schedule  
In the early training stages, large learning rate changes help the model explore a wide range of solutions, whereas, as training progresses, reducing fluctuations is crucial for stable convergence. To achieve this, varying the learning rate over epochs is an effective approach. A monotonically decaying learning rate starts high for rapid exploration and gradually decreases for stable convergence. Five learning rate decay schedulers were tested: constant, linear, exponential, cosine, and square root. Performance results are detailed in Table 5, generally showing improvements compared to the constant learning rate. Notably, all schedulers significantly reduced validation IoU noise in later epochs, enhancing stability and convergence, as illustrated in Figure 6.  
Among these, the cosine scheduler demonstrated the most promising results, consistently outperforming a constant learning rate. Its combination of early exploration with a final precise convergence phase proved highly effective. Consequently, it was selected as the preferred model configuration after straightforward hyperparameter optimization. In practice, while further iterations could refine parameters marginally, diminishing returns suggest that more parameter optimization would imply significant overfitting to the training data.  
Figure 5: IoU evolution for a ResNet with PReLU using (**a**) BCE and (**b**) DICE Loss.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Learning Rate Schedule** & & & & & \\ \hline
**Type** & \(\text{LR}_{\text{0}}\) & \(\text{LR}_{\text{end}}\) & **IoU** & **Precision** & **Recall** & **CCC** \\ \hline Constant & 0.0010 & 0.0010 & 0.802 & 0.908 & 0.874 & 0.834 \\ Linear & 0.0017 & 0.0006 & 0.795 & 0.912 & 0.862 & 0.831 \\ Exponential & 0.0017 & 0.0007 & 0.802 & 0.907 & **0.877** & 0.836 \\ Cosine & 0.0018 & 0.0003 & **0.804** & 0.914 & 0.872 & **0.839** \\ Square root & 0.0017 & 0.0004 & 0.803 & **0.919** & 0.865 & 0.836 \\ \hline \hline \end{tabular} Bold numbers indicate the best results obtained.  
\end{table}
Table 5: Performance metrics obtained using different learning rate schedules, detailing the schedule type, the initial learning rate (\(\text{LR}_{\text{0}}\)), and final learning rate (\(\text{LR}_{\text{end}}\)).
## 4 Experiments
### Research Question and Results
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline  & **LIMCR** & **SMOE** & **IHT** & **B-SMO** & **NCL** & **RUSB** & **EasyE** \\ \hline synapse-1.2 & 0.721 & 0.698 & 0.714 & 0.603 & 0.707 & 0.341 & 0.681 \\ jedit-3.2 & 0.692 & 0.669 & 0.703 & 0.709 & 0.671 & 0.377 & 0.639 \\ synapse-1.1 & 0.672 & 0.677 & 0.643 & 0.616 & 0.59 & 0.402 & 0.517 \\ log4j-1.0 & 0.76 & 0.619 & 0.595 & 0.666 & 0.779 & 0.26 & 0.6 \\ jedit-4.0 & 0.755 & 0.702 & 0.638 & 0.681 & 0.698 & 0.361 & 0.628 \\ ant-1.7 & 0.78 & 0.749 & 0.71 & 0.697 & 0.755 & 0.481 & 0.668 \\ camel-1.6 & 0.555 & 0.578 & 0.589 & 0.501 & 0.531 & 0.431 & 0.569 \\ camel-1.4 & 0.65 & 0.663 & 0.653 & 0.504 & 0.549 & 0.41 & 0.594 \\ Xerces-1.3 & 0.675 & 0.786 & 0.732 & 0.694 & 0.619 & 0.281 & 0.47 \\ Xalan-2.4 & 0.665 & 0.635 & 0.63 & 0.649 & 0.658 & 0.444 & 0.667 \\ jedit-4.2 & 0.757 & 0.759 & 0.673 & 0.73 & 0.699 & 0.699 & 0.75 \\ arc & 0.558 & 0.649 & 0.581 & 0.383 & 0.612 & 0.563 & 0.553 \\ synapse-1.0 & 0.741 & 0.749 & 0.711 & 0.658 & 0.539 & 0 & 0.678 \\ tomcat & 0.735 & 0.857 & 0.604 & 0.661 & 0.659 & 0.162 & 0.67 \\ camel-1.0 & 0.635 & 0.521 & 0.598 & 0.808 & 0.655 & 0.582 & 0.698 \\ \hline Average & **0.69** & 0.687 & 0.652 & 0.637 & 0.648 & 0.386 & 0.625 \\ [PERSON] rank & **2.533** & 2.6 & 3.8 & 4.2 & 3.833 & 6.633 & 4.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: G-mean of GNB with different imbalance learning methods on small datasets. From the average score and Friedman average ranks we see the combination of LIMCR and GNB still performs better than others except for G-mean, IHT+ABC ranks first on G-mean and LIMCR+GNB is slightly worse than it. [PERSON] test results are shown in Table 13, column named Total is the [PERSON] test among all nine methods, \(p\)-value of metric G-mean is 0.123 larger than 0.05 means there is no significant difference among all nine methods, i.e., LIMCR with other classifiers can perform as well as it with GNB and other methods for G-mean. The result of Nemenyi post hoc test (CD = 3.12, \(\alpha\) < 0.05) supports this result. Column LIMCR, SMOE and IHT represent the Friedman tests among classifiers with same resampling method, respectively, for instance, p-value in column LIMCR is the result of Friedman test among GNB, DTC, ABC with the same resampling method LIMCR. For LIMCR on balancedscore \(p\)-value is smaller than 0.05, which suggests the combination of LIMCR + GNB performs significantly better than other combinations of LIMCR. For other resampling methods, \(p\)-value of balancedscore and G-mean
## 4 Results and Discussion
### Leachability of Cr/Cr(VI) from Contaminated Soil in TCLP Leaching  
Figure 3 shows the Cr(VI) and Cr concentrations of the TCLP leachate. The Cr(VI) and Cr concentrations decreased with the increase of reductant addition. For the untreated soil, the Cr(VI) and total Cr leaching concentrations were approximately 38.8 mg/L and 40.4 mg/L, respectively, which exceeds the regulatory limit in the standards for hazardous wastes in China [34]. For the FeSO\({}_{4}\)-stabilized soil, the leached Cr(VI) and Cr concentrations decreased from 6.4 mg/L and 12.6 mg/L to 0.62 mg/L and 2.6 mg/L, respectively, when the reductant dosage was increased from 1% to 5%. For the CaS\({}_{5}\)-stabilized soil, the leached Cr(VI) and Cr concentrations decreased from 4.6 mg/L and 7.4 mg/L to 0.05 mg/L and 0.86 mg/L, respectively. In addition, with the same reductant dosage, the leached Cr(VI) and Cr concentrations of the CaS\({}_{5}\)-stabilized soils decreased more noticeably relative to those of the FeSO\({}_{4}\)-stabilized soils. These results clearly demonstrate the lower leachability of Cr in the CaS\({}_{5}\)-stabilized soil than in the FeSO\({}_{4}\)-stabilized soil under acidic solution conditions. This result agrees with most previous research results [10,13].  
Figure 3: Effect of reductant types and dosage on Cr/Cr(VI) concentration in the toxicity characteristic leaching procedure (TCLP) leachate. EPA, Environmental Protection Agency.  
Figure 2: Effect of reductant types and dosage on the redox potential of stabilized soils.
## 3 Validation of the Proposed Model
### How Stirrups Affect Corrosion Cracking
#### 3.3.2 Results: Effect of the Existence of Stirrups on the Width of Corrosion Cracks  
Figures 10 and 11 show the appearance of cracks on the cross-section of Specimens 3 and 4 for up to 200 steps. The crack in section 2 was confirmed to be larger (i.e., the center of the test body) than the cracks in sections 1 and 3. More specifically, the corrosive cracking was larger in section 2 than in the other two sections.  
Figure 12 shows the crack widths of the element on the concrete cover surface at the intersecting line of the cross-section of sections 1-3, as well as that of section 4 at the bottom of the test specimen, as calculated from Equation (14). The results for Specimens 3 and 4 are indicated by solid and dashed lines, respectively. Regardless of the presence or absence of the stirrup, the cracks in section 2 at the center of the test piece were the widest. Moreover, a comparison of Figures 10 and 11 confirmed that the range of damage in Specim 4 (without stirrup) was larger than that in Specim 3 (with stirrup). Subsequently, the setup of Specim 3 was believed to restrain the corrosion expansion of the crack, as evidenced by the narrower cracks. These results confirm that the stirrup influences (i) the damage inside the concrete and (ii) the width of the cracks generated on the concrete surface.
## 4 Numerical Simulation Analysis and Results
### Microscopic Numerical Simulation
#### 4.1.1 Microscopic Numerical Analysis in the Vehicle Starting Scenario  
Initial state: all vehicles started with an initial speed of 0, and the positions of Car 1 to Car 5 were 37.5 m, 30 m, 22.5 m, 15 m, and 7.5 m, respectively. Starting process: at time t = 0, the leading vehicle (Car 1) started to speed up with a initial acceleration 3 m/s\({}^{2}\) that decreases in magnitude to 0 until traveling at a constant speed. Following the start of Car 1, Car 2 to Car 5 started one after another by the extended IDM model proposed in this paper. And record and plot the acceleration changes of the five vehicles at 0.1 s intervals. Figure 5a-f shows the acceleration variations of the five vehicles over time with Car 5 being an RV (with each of four driver types) or an ACC or a CACCin the starting scenario.  
Figure 4: A schematic diagram of microscopic numerical simulation.  
Figure 3: The stability region of MTF.  
In the starting process, the maximum acceleration of Car 5 as an RV with each of four driver types was 0.8974 m/s\({}^{2}\), 0.9053 m/s\({}^{2}\), 0.9021 m/s\({}^{2}\), and 0.8906 m/s\({}^{2}\), respectively. From the simulation results, the maximum acceleration of Car 5 as an RV with a Type IV driver is the smallest, 1.2748% lower than that with a standard driver; the maximum acceleration of Car 5 as an RV with a type II driver is the largest, 0.3547% higher than that with a standard driver. The reason is that the Type IV driver maintains a low driving speed, while the Type II driver pursues a higher speed. The maximum acceleration of Car 5 as an ACC vehicle is 0.8984 m/s\({}^{2}\), slightly lower than that of Car 5 as an RV with the standard driver; the maximum acceleration of Car 5 as a CACC vehicle is 0.8703 m/s\({}^{2}\), 3.5251% lower than that of Car 5 as an RV with the standard driver and 3.1281% lower than that of Car 5 s an ACC vehicle. Therefore, Car 5 started more smoothly and steadily in the case of being a CACC vehicle other than an RV or an ACC vehicle. The results of the comparisons among four driver types and among the RV, ACC, and CACC vehicles are consistent with the results of the stability analysis in Section 3.
## 6 Conclusions  
In this study, experimental tests were conducted on a concrete slab using the Long Pulsed Thermography technique. Halogen lamps were used as the heat source. Eight square artificial delaminations of the same size were implemented at different known depths. In addition, reinforcement steel was also embedded inside the specimen. The main goals of this study were to assess the detectability capacity of delamination in terms of SNR, propose the nondimensional prefactor \(k\), predict the depth of delamination, and evaluate the effects of steel bars on the identification ability of defects. The following conclusions were achieved in this study.  
Active infrared thermography is an effective tool applied in nondestructive testing for the detectability of delamination. The delamination with concrete cover up to 7 cm in concrete structures can be identified successfully using SNR criterion.  
The SNR decreases moderately with an increase of delamination depth. On the other hand, the longer the heating time, the higher the SNR that can be obtained. It should be noted that in this study, the delamination with the depth of 8 cm was not identified.  
The nondimensional prefactor \(k\) was determined from the quadratic relationship between the depth of delamination and the observation time under chosen thermal diffusivities. The difference between depths expected from the estimated empirical prefactor \(k\) and real depths was approximately 7.20%, which proved the reliability of the estimated value of the prefactor.  
The effects of steel bars indicate that the SNR of a delamination above reinforcement is significantly higher than those under rebar. This means that defects located below reinforced steel are more difficult to detect than those above rebar.  
Conceptualization, J.H., C.K. and Q.H.T.; Methodology, J.H. and V.H.M.; Software, Q.H.T. and V.H.M.; Validation, J.-I.L. and C.K.; Formal Analysis, V.H.M.; Resources, K.-Y.L.; Data Curation, Q.H.T. and V.H.M.; Writing-Original Draft Preparation, V.H.M. and C.K.; Writing-Review & Editing, J.H. and Q.H.T., Supervision, J.H. and C.K.  
This research was funded by [Ministry of Oceans and Fisheries, Korea] project number [20180323] and [National Research Foundation of Korea] grant number [2017R1D1A3B03032854].  
This research was a part of the project titled 'Development of Design Technology for Safe Harbor from Disasters', funded by the Ministry of Oceans and Fisheries, Korea, and also supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (2017R1D1A3B03032854).  
The authors declare no conflicts of interest.