esa-sceva/satcom-chunk-collection huggingface dataset:
# A Two-Stage Kalman Filter-Based Carrier Tracking Loop for Weak GNSS Signals
## 5 Performance Evaluation
### Static Weak Signal Environment Simulation
#### 5.1.1 Sensitivity Test and Analysis
Comparing these results, the algorithms, 2-stage KF1 and 2-stage KF2, perform better for low C/\(N_{0}\) and lose the lock at approximately 19 dB-Hz and 15 dB-Hz, respectively. In contrast, the algorithms, 2-stage conv1 and 2-stage conv2, perform worse and lose the lock at about 29 dB-Hz and 23 dB-Hz, respectively. The algorithm, 2-stage KF1, improves the tracking threshold by approximately 10 dB relative to 2-stage conv1 under the same bandwidth and CIT. Similarly, 2-stage KF2 improves the tracking threshold by approximately 8 dB compared with 2-stage conv2. These results demonstrate that the proposed KF-based two-stage tracking methods outperform the conventional two-stage tracking methods due to the adaptively KF gains in the fine tracking stage when the signal strength decreases. Comparing the 2-stage conv1 and 2-stage conv2 results, 2-stage conv2 can track the signal until approximately 680 s, when the signal, C/\(N_{0}\), reaches the 23 dB-Hz level, while 2-stage conv1 can track the signal at 29 dB-Hz. The tracking threshold of 2-stage conv2 is improved by approximately 6 dB, which is mostly because 2-stage conv2 has a longer CIT and a narrower PLL bandwidth in both the coarse and the fine tracking stages than 2-stage conv1. The narrower bandwidth introduces less noise, and the longer CIT can help track weaker signals. Compared with 2-stage KF1 and 2-stage KF2, the threshold of 2-stage KF2 is improved by only 4 dB. The reason for this threshold improvement is also the longer CIT and narrower bandwidth. However, the sensitivity improvement in the proposed algorithm is not as noticeable as the conventional algorithm with a 6 dB improvement. Although the coarse tracking stage utilizes the traditional FLL-assisted-PLL tracking loop, the sensitivity of the tracking loop mainly depends on the fine tracking stage once bit synchronization has been achieved. Therefore, when C/\(N_{0}\) decreases to a low level, the sensitivity of the proposed tracking loop mainly depends on the time-varying Kalman gains in the fine tracking stage. Figure 5: Tracking sensitivity, Doppler frequency, and tracking thresholds estimated by the four tracking algorithms for PRN 14 satellite signals under static weak signal environments. (PRN: pseudo-random number). In summary, under the same bandwidths and CITs, the proposed method exhibits a substantial performance advantage over the conventional method in terms of the tracking sensitivity. Comparing the 2-stage KF1 to 2-stage conv2, it shows that even with a shorter integration time, the sensitivity of the proposed method is superior to that of the conventional approach. Furthermore, the longer CIT and narrower bandwidth can also improve the sensitivity of the proposed method.
# Adaptive Emergency Call Service for Disaster Management
## 5 Simulation Results
### Performance Analysis
#### 5.2.1 Scenarios and Tests  
Extreme emergency scenarios are considered to assess the performance of 5G-SOS. In particular, success rate, \(EEL\), \(\phi\), and \(\beta\) metrics under extreme emergency call duration, device density, data size, and emergency call number are studied.
## 4 Applied Strategies for Textile Dye Wastewater Treatment
### Physical Treatment Process
#### 4.1.1 Adsorption
\begin{table}
\begin{tabular}{c c c c} \hline
**Textile Dyes** & **Treatment Methods** & **Type of the Treatment Methods** & **References** \\ \hline \multirow{4}{*}{Rhodamine B} & Adsorption by micro and & & [82] \\  & nano-particles of ZnO & Physical & \\ \cline{2-3}  & Adsorption with treated rice & & [83] \\  & husk-based activated carbon & & \\ \cline{2-3}  & Ozonation & Chemical & [84] \\ \hline Basic violet and Acid blue 93 & _Pseudomonas putida_ & Biological & [85] \\ \hline \multirow{4}{*}{Basic violet 3} & _Candidia krusei_ & Biological & [86] \\ \cline{2-3}  & Fenton oxidation & & [87] \\ \cline{2-3}  & Ag, ZnO and bimetallic Ag/ZnO & Physical & [88] \\  & alloy nanoparticles & & [88] \\ \hline Reactive Black 5 and Reactive red & Nano zerovalent iron treatment & Physical & [89] \\ \hline Reactive red 195 & Electro-fenton & Physical & [90] \\ \hline \multirow{4}{*}{Reactive red 2} & Adsorption sludge & Physical & [91] \\ \cline{2-3}  & _Pseudomonas_ sp. SUK1 & Biological & [92] \\ \hline Reactive red 180 & _Citrobacter_ sp. CK3 & Biological & [93] \\ \hline Reactive red 198 & Catalytic ozonation & Chemical & [94] \\ \hline \multirow{4}{*}{Reactive green} & _Microoccus glutamicus_ & & [95] \\  & NCIM-2168 & Biological & [96] \\ \cline{2-3}  & White rot fungus & & [97] \\ \cline{2-3}  & UV/H2O2 advanced oxidation & & [98] \\ \cline{2-3}  & process (AOP) & Physical & [98] \\ \hline \multirow{4}{*}{Indigo carmine} & Electrooxidation on & & [99] \\  & Ti/IrO2-SnO2-Sb2O3 & & [100] \\ \cline{2-3}  & Electrochemical oxidation & Physical & [101] \\ \cline{2-3}  & Adsorption with & & [101] \\ \cline{2-3}  & calcium hydroxide & & [101] \\ \cline{2-3}  & _Tranetes hirsuta_ & & [102] \\ \cline{2-3}  & Increase production & & \\ \cline{2-3}  & _Planerochaet chrysosporium_ & & \\ \cline{2-3}  & manganese & Biological & [103] \\ \cline{2-3}  & peroxidase production & & \\ \cline{2-3}  & _Bacillus amyloliquefaciens_ & & [93,104] \\ \cline{2-3}  & lactose production & & \\ \hline \multirow{4}{*}{Anthraquinone, indigo and triphenylmethane} & _Ganoderma_ sp. En3 & Biological & [105] \\ \cline{2-3}  & _Armillaria_ sp. F022 & Biological & [106] \\ \cline{2-3}  & Chitosan adsorption & Physical & [107] \\ \hline Acid red 131 & Electrochemical coagulation & & [108] \\ \hline Acid red 73 & Coagulation & & [109] \\ \hline Methyl violet, basic fuchsin and their mixture & Biosorption using fungal biomass & Biological & [110] \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._
# On-the-Fly Repairing of Atomicity Violations in ARINC 653 Software
## 5 Experiments
### Performance Evaluation
The cause of the overhead is that, after diagnosing an atomic violation by examining the locking discipline, it waits until the execution of the lock is finished before performing treatment. Unnecessary treats can also cause overhead to increase due to many false positives. RAV has a higher diagnostic time than [PERSON] et al. 's [17] algorithm but has lower overhead because it resumes the stopped thread as soon as the variable executes  
Figure 10: Memory usage of synthetic programs [16]. correctly. As a result, the proportion of treatment time in the total repairing time shows an average of 0.13%, a maximum of 0.18% for \(T_{1}^{[R-R]}\parallel T_{2}^{W}\) and a minimum of 0.05% for \(T_{1}^{[W-R]}\parallel T_{2}^{W}\). Summarizing the results, RAV collected incorrect interleaving with a low probability when training a program in which the interleaving of shared variables was complex. Therefore, it is necessary to perform sufficient training and check the correctness of the collected interleaving. RAV repaired all five types of atomicity violations in real-world software. It showed an average of 1.13\(\times\) time overhead and a constant space overhead of 219 KB. Most of the repair time was spent on diagnosis and the time required for action was very low. Therefore, RAV can be applied to airborne software to repair atomicity violations with low overhead. Succeeding researchers can focus on automatic verification or collection methods to increase the accuracy of correct interleaving collection in training. In addition, we plan to improve the algorithm to be repaired in RAV by analyzing multi-variable atomicity violations that manifest in real-world software.
# Evaluating Levels of Automation in Human-Robot Collaboration at Different Workload Levels
## 3 Results
### QoT Execution
#### 3.1.2 Efficiency  
The interaction of LOA and workload had a significant effect on completion time (F (3, 152) = 4.838, \(p\) = 0.003). At high LOA and LWL, participants completed the task at shorter time compared to the other combinations. LOA also had significant effect on the completion time (F (1, 152) = 136.565, \(p\) < 0.001) with the high LOA (M = 87.3, SD = 0) having lower completion time compared to the low LOA (M = 107.945, SD = 16.547) as expected, even though the users had the option to stop the robot's operation at any point in the high LOA mode, thereby increasing the completion time. Workload also had significant effect on the completion time (F (3, 152) = 4.838, \(p\) = 0.004) with the LWL (M = 94.62, SD = 9.028) having less completion time compared to the HWL (M = 103.158, SD = 23.924). Higher task complexity (MWL1, M = 96.449, SD = 12.766) resulted in less completion time compared to the workload caused by the secondary task (MWL2, M = 96.595, SD = 11.241).
# Enhancing Kidney Disease Diagnosis Using ACO-Based Feature Selection and Explainable AI Techniques
## 5 Experimental Results
### Performance Comparison with State-of-the-Art Techniques  
Figure 8 compares the proposed approach with the previous most advanced KD prediction strategy [26], which obtained the previous best accuracy of 93.29% in identifying KD patients. With the same clinical KD dataset, which included 1,023,426 samples with a 1.20% positive ratio, the proposed approach significantly outperformed the previous benchmark by 4.41% and achieved a 97.70% accuracy.
# Image-Based Arabian Camel Breed Classification Using Transfer Learning on CNNs
## 4 Experimentation
### Pre-Trained CNNs and Hyperparameter Settings  
The CNN architectures utilized in this project were pre-trained using the ImageNet dataset which contains millions of images from a thousand classes. There are images of Arabian camels and other similar animals in the ImageNet dataset; however, it does not include breeds of the Arabian camel.  
The CNN framework we propose in this work for Arabian camel breed prediction consists of four layers: an input layer, a hub layer, a dropout layer, and a dense layer. The input layer takes images as an input. The hub layer is a pre-trained model retrieved from the TensorFlow Hub as the base model in our application framework. The dropout layer randomly drops out 20% of the units to prevent overfitting. Dropout is actually a method for regularizing neural networks and involves randomly changing some features to 0 during the forward pass [38]. The dense layer outputs a vector of a length equal to the number of classes, with a L2 regularization of 0.0001.  
The experimented CNNs' hyper-parameters are configured as follows. A stochastic gradient descent (SGD) with a learning rate of 0.005 and a momentum 0.9 is used in all experiments as the optimization method because it produced the best results when compared to other optimizers we experimented with such as Adam, Adamax, Adadelta, Nadam, Adagrad, RMSprop, and Ftrl. Moreover, the batch size is set to 16 but for large models we set it to 8; the learnable parameters are initialized using Keras' default values. The loss function is the categorical crossentropy with logits and a label smoothing of 0.1. The metric used to evaluate the model is the accuracy.  
All models (each incorporating a different base model) are trained for 200 epochs. However, we used an early stopping callback function that monitors the validation loss and stops the training if it does not improve for 10 consecutive epochs.
## 5 Link Budget Analysis for the Measurements of Non-Cooperative Targets
Combining (30) and (35), the load-to-noise ratio equation of the non-cooperative spacecraft tracking and measurement link in Earth-Moon space based on the CEI and BeiDou interplanetary link can be finally expressed as follows:  
\[C/N_{0}=C-N_{0} \tag{30}\]0  
Let the observation matrix of the fixing process of this method be \(H\), the eigenvalues of \(H^{T}H\) be \(\lambda_{i}(i=1,2,\ldots,j)\), and \(PDOP_{BD\to space\to CEI}\) be the geometric accuracy of the full process of fixing. The Range Accuracy Evaluation of Non-Cooperative Spacecraft in Earth-Moon Space Based on CEI and BeiDou Interplanetary Link  
The non-cooperative spacecraft ranging accuracy in Earth-Moon space based on the CEI and BeiDou interplanetary link is another important metric to judge the effectiveness of this method, which can be expressed as follows:  
\[C/N_{0}=C-N_{0} \tag{30}\]1  
where \(\sigma_{clock\_error}\) is the BeiDou interstellar link clock difference; \(\sigma_{ ephemeris\_error}\) is the BeiDou interstellar link ephemeris error; \(\sigma_{multipath\_error}\) is the multipath error of the BeiDou interstellar link throughout transmission; \(\sigma_{receiver\_error}\) is the receiver noise and resolution error of the CEI receiver array; and \(\sigma_{thermal\_error}\) is the thermal noise code tracking error, which is a jitter function of the altitude, acquisition, and tracking threshold, and the signal chip rate of the non-cooperative spacecraft.
## 4 Discussion
### Influence of Vegetation Type on the Performance of CT Model Modified under Hypothesis II  
Our study indicated the approach developed under hypothesis II was effective for modifying model thresholds of SFs for mapping aquatic vegetation types in Taihu Lake with HJ-CCD imagery for all tested date combinations. With the approach it is crucial to select good representative sample regions for each vegetation type to modify model thresholds. For example, we selected five regions of interest  
Figure 11: _Cont._for floating-leaved vegetation from the map classified for 11 July to test effects on a modified model for 16 August. The regions were equivalent in size, but had different percent cover by floating-leaved vegetation (Figure 11 and Table 12). SF\({}_{2}\) values for pixels in the five regions of interest for images for 11 July and 16 August were extracted and sorted in descending order and five linear models were established to adjust SF\({}_{2}\) values for modeling August aquatic vegetation classes. We also calculated differences between SF\({}_{2}\) values for pixels from the traditional and modified models (Table 12).  
The results suggested that regions of interest covered by larger proportions of floating-leaved vegetation would lead to smaller difference between SF\({}_{2}\) values in the modified and traditional CT models. Therefore, we should select sample regions with pure pixels of the target vegetation type to the extent possible to develop a better modified CT model with the approach under hypothesis II.  
harvesting and restoration, especially in the growth stage of aquatic vegetation (from June to October). Past research has also suggested that aquatic vegetation harvesting can be a very cost-effective method for removing excessive nutrients and protecting lakes from this adverse shift. Through such efforts, nutrients contained in the plant tissues are removed from lake ecosystem [36; 37; 38]. Combined with enough ground-truth samples, a CT model can be developed based on remote sensing images to monitor the distribution of aquatic vegetation. In most cases there are adequate remote sensing images available to monitor lakes through time, but a lack of corresponding ground-truth samples. It can therefore be difficult to develop a corresponding CT model to accurately map the distribution of aquatic vegetation. In this study, results showed an approach based on a linear relation of ranked values of spectral features for two image dates offered a feasible approach for mapping aquatic vegetation form remote imagery when corresponding ground-truth samples were not available. This provides the means to monitor the composition and distribution of aquatic vegetation types in shallow lake in near-real time to inform lakes management decisions.
## 4 Fault Current Limiting Devices
### Non-Superconducting FCL
#### 4.1.6 Resonance Type FCL: Parallel Resonance Type FCL  
In [31], as shown in Figure 8b, a parallel resonance type FCL has been investigated and compared to the bridge type FCL with parallel resistive-inductive shunt impedance. The idea of parallel resonance type FCL has been proposed in [32]. In normal operation, the semiconductor switch is in the on state and the parallel resonance is bypassed. After a fault occurrence, the semiconductor switch goes to the off state. So, the fault current passes through the parallel impedance and is limited by the resonance type FCL, which helps to enhance the FRT capability of the DFIG. Considering the assessment factors, including the deviation of the wind farm terminal voltage, the active power of the wind farm, and the rotor speed of the DFIG, the parallel resonance type FCL has better operation in comparison with the bridge type FCL with parallel resistive-inductive shunt impedance.
## 3 Related Works
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Reference** & **Year** & **Reliability** & **Delay** & **Coverage** & **Power Loss** & **Efficiency** \\ \hline  
& ✗ & ✗ & ✓ \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison table of related work. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Reference** & **Year** & **Reliability** & **Delay** & **Coverage** & **Power Loss** & **Efficiency** \\ \hline
[46] & 2024 & ✗ & ✗ & ✓ & ✓ & ✗ \\ \hline
[47] & 2024 & ✓ & ✗ & ✗ & ✗ & ✗ \\ \hline
[48] & 2024 & ✓ & ✗ & ✗ & ✗ & ✗ \\ \hline
[49] & 2024 & ✓ & ✗ & ✗ & ✓ & ✗ \\ \hline
[50] & 2024 & ✓ & ✗ & ✗ & ✗ & ✗ \\ \hline
[51] & 2024 & ✓ & ✗ & ✗ & ✗ & ✗ \\ \hline
[52] & 2024 & ✓ & ✗ & ✗ & ✗ & ✗ \\ \hline
[53] & 2024 & ✗ & ✗ & ✗ & ✗ & ✓ \\ \hline
[54] & 2024 & ✗ & ✗ & ✓ & ✗ & ✗ \\ \hline
[55] & 2024 & ✗ & ✗ & ✗ & ✗ & ✗ \\ \hline This work & 2024 & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Definition** \\ \hline \(M=15\) & Number of HRIS elements \\ \hline \(l_{M}\) & 10 (cm) \\ \hline \(N_{\theta}\) & Number of phase shifts, where \(N_{\theta}=\{0,\ldots,2n\pi/N_{\theta},\ldots,2(N_{\theta}-1)\pi/N_{\theta}\},1 \leq n\leq N_{\theta}-1\) \\ \hline \(U_{AG}^{n}\) & User antenna gain \\ \hline \(S\) & Power density \\ \hline \(Z_{air}\) & Air impedance \\ \hline \(E_{r}\) & Electric field \\ \hline \(A_{e}\) & Effective aperture \\ \hline \(A_{HRIS}\) & HRIS effective aperture \\ \hline \(P_{s}\) & Signal power \\ \hline \(l_{HRIS}^{ll}\) & Distance between HRIS and user \\ \hline \(l_{HRIS}^{BS}\) & Distance between HRIS and BS \\ \hline \(NP^{BS}\) & BS normalised power radiation \\ \hline \(NP^{ll}\) & User normalised power radiation \\ \hline \(NP^{HRIS,BS}\) & HRIS normalised power radiation from BS \\ \hline \(NP^{HRIS,ll}\) & HRIS normalised power radiation directed toward user \\ \hline \(A_{r,t}\) & Signal amplitude response \\ \hline \(CSI_{inst}\) & Instantaneous channel state information \\ \hline \(CSI_{stat}\) & Statistical channel state information \\ \hline \(Iresponse\) & Impulse response \\ \hline \(DF\) & Digital filter \\ \hline \(FD\) & \multicolumn{2}{c}{Fading distribution} \\ \hline \(CH_{gain}\) & Channel gain \\ \hline \(R_{M
## 4 Performance Evaluation
### Objective Evaluation  
As mentioned earlier, there were two objectives measured here. One was the RMSE between the reference HRTF \(y(n)\) and the estimated HRTF \(\hat{y}(n)\), which was defined as  
\[\text{RMSE}(y,\,\hat{y})=20\log_{10}\sqrt{\frac{1}{N}\sum\limits_{n=0}^{N-1} \left(y(n)-\hat{y}(n)\right)^{2}} \tag{2}\]  
where \(N\) (=200) was the total length of the HRTF. In addition, the LSD was defined as  
\[\text{LSD}(Y,\,\hat{Y})=\sqrt{\frac{1}{M}\sum\limits_{k=0}^{M}\left(20\log_{ 10}\frac{|Y(k)|}{|\hat{Y}(k)|}\right)^{2}} \tag{3}\]  
where \(Y(k)\) and \(\hat{Y}(k)\) were obtained by applying a fast Fourier transform (FFT) to \(\text{y}(n)\) and \(\hat{y}(n)\), respectively, and \(M\) (=512) was half the size of the FFT.  
Figure 6 compares the RMSEs of the individual subject according to different HRTF estimation methods, measured at \(-135^{\circ}\), \(-80^{\circ}\), and \(-45^{\circ}\). Note that the performances of the HRTFs at \(135^{\circ}\), \(80^{\circ}\), and \(45^{\circ}\) were identical to those at \(-135^{\circ}\), \(-80^{\circ}\), and \(-45^{\circ}\), respectively. In addition, Table 2 compares the average HRTF over all the HRTFs in the CIPIC database, measured at \(-135^{\circ}\), \(-80^{\circ}\), and \(-45^{\circ}\). As shown in Figure 6 and Table 2, the DNN-based method (DNN(37) HRTF) and the proposed method provided RMSEs that were 1.91 and 0.88 dB higher than the average HRTF method, respectively. Even though the average HRTF had the lowest RMSE because this was obtained from exact pinna measurements, it was actually hard to get pinna measurement data from live human ears. Without such pinna measurements, the DNN-based method increased RMSE by 0.02 dB. Thus, the RMSE of the estimated HRTF by the proposed method was lowered than that estimated by the DNN(17) HRTF.  
Next, the LSD was computed between the reference HRTF and its estimated HRTF for each of the four different HRTF estimation methods. Figure 7 and Table 3 compare the LSDs of the individual subject and the average LSD according to different HRTF estimation methods, measured at \(-135^{\circ}\), \(-80^{\circ}\), and \(-45^{\circ}\), respectively. As shown in the figure and table, the average LSD measured from the DNN(37) HRTFs was lowest among the methods. However, similar to the RMSE described above, the average LSD of the HRTFs estimated by the proposed method was lower by 0.85 dB compared to that by DNN(17) HRTFs. In particular, the LSDs for the HRTFs estimated by both neural network-based methods were greatly reduced compared to the average HRTF method.
## 2 Methodology  
Figure 1 provides a schematic overview of the methodology employed in this study. The input data collection phase initially involves gathering data, including real-time output power from PV panels observed in a laboratory setting throughout a typical day. Subsequently, the study introduces the V2 GSUN method, which incorporates a Fuzzy Logic controller (FLC). The key goal of this control system is to optimize the time window size of the BESS to smooth out power fluctuations in real-time effectively.  
Under experimental conditions, the isolated power system is expected to experience significant frequency variations due to handling highly variable power from renewable generation. The operating procedures for grid networks are generally more lenient with island system frequency standards than bulk power systems. This is due to the oper  
Figure 1: General scheme of the proposed methodology. Reproduced with permission from [33].  
ational complexity of such systems, especially when integrating renewable distributed energy resources.  
To accomplish this objective, the experimental microgrid's data logger is used to monitor the supervisory control of the BESS. The proposed method's effectiveness is assessed in comparison to conventional techniques such as the MA and traditional ramp-rate (R-R) methods. For experimental testing purposes, a BESS is employed to replicate the battery characteristics of an EV, specifically a BYD T3 electric van available in the laboratory. This battery is known for its high energy storage density and capability to meet performance requirements effectively. The EV's BESS is integrated into the grid using a 50 kW bidirectional three-phase inverter, applying the proposed method within the computerized control system. The results obtained are rigorously validated through experiments under diverse technical criteria, supplemented by a detailed analysis of variability and energy dynamics. This comprehensive assessment underscores the reliability and practicality of the proposed methodology in real-world applications.