esa-sceva/satcom-chunk-collection huggingface dataset:
# Explainability and Interpretability in Concept and Data Drift: A Systematic Literature Review
## 6 Taxonomy
### Change-Point and Statistical-Test Theory
#### 6.2.1 Technical and Methodological Insights
A spike in \"average service time\" is not an isolated alert but the second act of a story begun weeks earlier, when \"clerk workload surged\"--a link proven by Granger causality. The resulting paired drift, stamped with lags and feature names (\"workload explains service time at \(p<0.01\)\"), transforms statistical output into accountable diagnosis. Stored in color-coded diaries (gray for unexplained, blue for causal), these sequences let managers trace systemic levers across incidents, turning isolated detections into chapters of an ongoing operational saga. Across this progression--from agent-annotated thresholds to causal pairs--the class reveals its unifying genius: statistical change-point methods are not detectors but narrators. Parameters like \(\delta\) chronicle environmental noise; Shapley vectors document shifting feature alliances; Polya Trees cartograph instability; embeddings retrieve historical echoes; and causality tests script blame. By binding detection to domain semantics (sensor IDs, feature values, spatial bins, event logs) and prioritizing visuals over metrics, every framework ensures alerts arrive not as cryptic warnings but as storied diagnoses: where change ignited, which elements reconfigured, why it matters, and--in the most advanced cases--how long the fuse burned. This seamless fusion of statistical rigor and narrative intuition distinguishes the class, turning change-point theory into a language of actionable insight. The explanations provided are interpretive aids rather than intrinsic to the predictive mechanism itself, making this class a clear example of explainability over interpretability, fitting it into the XAI cluster.
###### Abstract  
The goal of this paper is to show that super-gain optical parametric amplification can be achieved even in a small micro-resonator using high-intensity ultrashort pump waves, provided that the frequencies of the ultrashort pulses are tuned to maximize the intracavity magnitude of the wave to be amplified, which we call the stimulus wave. In order to accomplish this, we have performed a dispersion analysis via computational modeling of the electric polarization density in terms of the non-linear electron cloud motion and we have concurrently solved the electric polarization density and the wave equation for the electric field. Based on a series of non-linear programming-integrated finite difference time-domain simulations, we have identified the optimal pump wave frequencies that simultaneously maximize the stored electric energy density and the polarization density inside a micro-resonator by using the Broyden-Fletcher-Goldfarb-Shanno (BFGS) optimization algorithm. When the intracavity energy and the polarization density (which acts as an energy coupling coefficient) are simultaneously high, an input wave can be strongly amplified by efficiently drawing energy from a highly energized cavity. Therefore, we propose that micrometer-scale achievement of super-gain optical parametric amplification is possible in a micro-resonator via high-intensity ultrashort \"pump wave\" pulses, by determining the optimal frequencies that concurrently maximize the stored electric energy density and the polarization density in a dielectric interaction medium.  
optical parametric amplification; non-linear wave mixing; micro-resonator; optimization
## 2 Data Collection and Analysis  
### Data Collection  
A UAV was deployed to capture aerial video footage of critical sections along the mainline, including deceleration lanes and diversion areas, at the investigated interchanges. For optimal data collection, the chosen locations should exhibit clear weather conditions with minimal wind to ensure unobstructed visibility. Selected shooting sections should also feature comprehensive and distinct signage and markings, free from traffic congestion, accidents, and facility maintenance, thereby minimizing disruptions to drivers.  
The UAV employed an advanced O3 mapping solution integrated with the DJI Fly App, facilitating seamless real-time output of high-definition video data. This enhanced the ease of observation for the operator, enabling prompt adjustment of survey parameters as necessary (Figure 1). Throughout the survey process, the UAV ascended to a height of 200 m directly above the designated survey areas, capturing video footage perpendicular to the road sections. Aerial photography sessions were conducted four times during peak hours, with each consecutive session spanning 15 min.
## 3 Method
### Temporal Correction and Data Synchronisation
While in the original scans, in Figures 6a,d,g, the green marked characteristic wake structures have the same downstream position for the time interval (\(T_{n}\)), it can be seen in the corrected data in Figure 6b,e,h that the positions of the red marked structures differ clearly from the green markings at individual points in time (\(t\)). We chose these three time points in order to show the effect of temporal alignment using the most common time sampling types, which set the time stamp either at the beginning of the measurement (Figure 6a-c), at the middle of the scan (Figure 6d-f), or at the end of the scan (Figure 6g-i). From the different positions of the green and red markers and with regard to the large wind-speed deviations in Figure 6c,f,i, we emphasise the relevance of the temporal correction again. However, the extent to which the temporal correction influences the results of the analysis of dynamic processes, like wake centre tracking or load time-series correlation, was not within the scope of the results in Section 4 and should be investigated in other studies. Figure 5: Temporal alignment of a PPI scan at time \(t\). Dark-blue lines indicate LiDAR measurements, light-blue lines indicate the propagation steps, green lines indicate the measurement reset time (\(t_{r}\)), and the red line marks the time of interpolation. This example shows \(\mathrm{N}_{\phi}=11\).
## 2 Materials and Methods
### Data Analysis  
Data analysis was focused on the investigation of differences between MAP and MAP-PAD samples at the same time point (high-quality or end of high-quality). Thus, data were analyzed for each species, seabream or seabass, and each processing type, gutted fish or fillet, separately within each time point (HQ or End). The latter was decided in order to follow the study design of testing different time points on different days.  
Preference data for the raw samples are reported as frequencies and were analyzed by applying a binomial test. Sensory freshness data were checked for normality and were found not to be normal (Shapiro-Wilk test, \(p<0.05\) and visual observation of histograms and P-Plots). Thus, the Mann-Whitney U test was run to test differences in the scores of the attributes between samples with and without a pad, at high quality and at the end of high-quality time points. Attributes were treated as dependent variables, while presence or absence of a pad was treated as an independent variable, and data were analyzed at high quality (HQ) and end of high quality (End) time points, separately. The chi-square test was used for categorical variables, such as presence or absence of liquid in the packaging, mucus on the skin, and coherency of the tissue, as well as consumer attitudes and frequency of consumption.  
Volatile data were analyzed by means of two-way ANOVA considering the presence or absence of pad and high-quality or end of high-quality time points as main effects, and including their interaction effect. Volatiles for which a significant effect (\(p<0.05\)) or a tendency for significance (\(p<0.1\)) was shown, as well as significant freshness perception attributes from the consumer study as supplementary data, were subjected to principal component analysis (PCA). Chemical freshness data (K-values) were analyzed by a Mann-Whitney U test, as described above for sensory freshness data, because data were not normally distributed.  
All analyses were based on the 5 % significance level (\(\alpha=0.05\)). IBM SPSS Statistics Version 28 (IBM Corporation, Armonk, New York, NY, USA) was used for the binomial, Mann-Whitney U, chi-square tests, as well as for the two-way ANOVA. PCA was run in XLSTAT 2022 (Addinsoft, New York, NY, USA).
## 2 System Description
### PLL\({}^{+}\) and PLL\({}^{-}\) Control Structure  
Figure 3 gives the control structure of PLL\({}^{+}\) and PLL\({}^{-}\). The PLL\({}^{+}\) and PLL\({}^{-}\) take positive and negative sequence voltages of PCC as inputs, respectively, and output the positive and negative sequence angular frequency \(\omega_{\text{pll}}\)\({}^{+}\), \(\omega_{\text{pll}}\)\({}^{-}\), and phase angle \(\theta_{\text{pll}}\)\({}^{+}\), \(\theta_{\text{pll}}\)\({}^{-}\).
## 5 Experimental Results  
### Experimental Results of the Case Study  
The carbonation estimation model was trained using the input data. The model was then verified by maintaining the training and validation data ratio as 80:20. We used the random extraction method and the Adam learning method; the number of iterations was set to 100. The coefficient of determination and RMSE were selected as the performance indicators of the carbonation model. Typically, the coefficient of determination denotes the proportion of variance of the response variable, which can be explained by the applied model. In general, a coefficient of determination greater than 0.5 implies that the predictor variable can statistically predict the response variables. Table 5 and Figure 8 present the training and validation results. The learning time was 556.20 s. The hardware specifications were Intel i7-7700K CPU 4.20 GHz and NVIDIA GeForce GTX 960. The coefficient of determination of the training data and the RMSE were 0.638 and 4.572, respectively. Conversely, the coefficient of determination of the verification data was 0.504, and the RMSE was  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number** & **Layer Name** & **Hidden Unit** & **Activation Function** & **Number of Parameters** \\ \hline
1 & LSTM & 30 & tanh & 5400 \\ \hline - & Dropout & - & - & \\ \hline
2 & LSTM & 30 & ReLU & 7320 \\ \hline - & Dropout & - & - & \\ \hline
3 & LSTM & 30 & tanh & 7320 \\ \hline - & Dropout & - & - & \\ \hline
4 & Dense & 1 & Linear & 31 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Long short-term memory (LSTM) model layer for the carbonation model.  
Figure 7: Proposed LSTM model for generating the carbonation models.  
5.057. Thus, the coefficients of determination for both the training and validation datasets exceeded 0.5.  
The cross-validation was conducted to check for overfitting of the proposed model. Cross-validation verifies whether a particular dataset is overfitted when the dataset has a small size. The k-fold method was used for cross-validation. The k-fold cross-validation divided the training dataset into four folds, and a total of five folds were included in the validation dataset. The distribution method was randomly selected. Figure 9 shows the results of the cross-validation. The average of the cross-validation result was 0.5028, which was slightly different from the first experimental result. This result verified that the experimental results were not overfitting for a specific dataset.  
Figure 8: Carbonation prediction results of the LSTM model for the (**a**) training and (**b**) validation datasets.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Performance Indicator** & **Value** \\ \hline Training dataset \(R^{2}\) & 0.638 \\ \hline Training dataset RMSE & 4.572 \\ \hline Validation dataset \(R^{2}\) & 0.504 \\ \hline Validation dataset RMSE & 5.057 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance indicators of the carbonation model.  
Figure 9: The k-fold cross-validation test results of the proposed model.
## 2 Materials and Methods
### Mass Balance  
A comprehensive study on the removal mechanisms of EDCs by microalgae was conducted to assess the contribution of bioadsorption, bioaccumulation, and biodegradation pathways. The biodegradation percentage of each EDC can be calculated as follows:  
\[P(\%)=(A_{t}-A_{r}-A_{d}-A_{a}-A_{c}).\frac{100}{A_{t}} \tag{1}\]  
where \(A_{t}\) is the initial amount of each EDC added to the medium (\(\mu\)g at day 0). \(A_{r}\) is the amount of EDCs in the supernatant in the LOM experiments. \(A_{d}\) is the amount of EDCs adsorbed on the microalgal cell wall. As mentioned above, the samples from the LOM experiments were centrifuged, and the microalgae cells were harvested and separated from the supernatant. This biomass was washed with 1 mL of water and centrifuged at 3500 rpm for 20 min (model 5810, Eppendorf, Hamburg, Germany). The wash water was discarded, and cell pellets were resuspended in 1 mL of methanol. Finally, the pellets were sonicated for 15 min (37 kHz, 820 W) in an Elmasonic P bath (Elma Schmidbauer, Singen, Germany) to release the EDCs adsorbed on the cell wall into the methanol. The mixture was then centrifuged again, and the biomass was separated from the organic solvent containing the adsorbed EDCs. These samples were used to quantify the concentration of adsorbed EDCs using previously established HPLC methods. Finally, \(A_{c}\) is the amount of EDCs bioaccumulated inside the microalgal cells. To determine this value, 1 mL of methanol was added to the harvested cell pellet after the last centrifugation, and the solution was kept under stirring at 900 rpm overnight. To ensure cell wall lysis, the samples were sonicated for 1 h and then centrifuged at 3500 rpm for 20 min. The concentration of EDCs bioaccumulated by the microalgae was determined from the resulting supernatant using HPLC. Finally, \(A_{a}\) is the amount of EDCs removed by abiotic mechanisms. The results of the supernatant analysis in the LO experiments were used to estimate the removal fraction of the EDCs by abiotic processes.  
The kinetic constants for MeP, PrP, BuP, BP, BPA, and E removal were also calculated for the O, LO, and LOM experiments using a pseudo-first-order model as follows:  
\[LnC_{t}=-kt+LnC_{0} \tag{2}\]  
where \(C_{0}\) is the initial concentration (ppb) of each pollutant at day 0, \(C_{t}\) is the EDC concentration (ppb) at time \(t\), \(k\) is the removal rate constant (h\({}^{-1}\)), and \(t\) is the reaction time (h).
## 3 Data-Constrained Modelling for the Samples
### Data-Constrained Modelling
Because the theoretical linear X-ray absorption coefficients of the compositions in the samples were obtained based on the equivalent energies from each experiment, the equivalent energy errors caused by the acceleration voltage control feedback system did not introduce additional DCM calculation errors. Equivalent energy errors due to image artifacts may also lead to a decrease in DCM accuracy, but quantifying this impact is challenging. In Equation (1), \(\text{S}^{(\text{m})}\) represents the self-energy parameter of composition m. When the volume fraction of a certain composition in the sample was known, the parameter corresponding to that composition was adjusted to ensure that the calculated volume fraction matched the known value. This improved the accuracy of the DCM calculations. For sample 1, this parameter was set to 0. For samples 2-6, due to pronounced partial volume effects and metal artifacts on their CT slices, the DCM calculation results had significant deviations from the known values when the self-energy parameters were set to 0. For these samples, the self-energy parameter of silica was adjusted to ensure that the volume fractions calculated by DCM matched the known values. \(\text{E}_{\text{n}}\) in Equation (1) represents the interaction parameters [30], which were set as the default values of the DCM software [34] to reduce the impact of imaging noise on the DCM calculations. Reference [30] provides a detailed introduction to the numerical calculation methods, and the interaction energy parameters and self-energy parameters of DCM. The DCM software integrates DCM calculation capabilities, an X-ray absorption coefficient database, and some commonly used image processing functions, which can be freely downloaded from the URL provided in reference [34]. The DCM calculation parameters \(\mu^{(\text{m})}\) and \(\text{S}^{(\text{m})}\) for samples 1-6 are shown in Table 4.
# A New Method to Determine the Steel Fibre Content of Existing Structures--Evaluation and Validation
## 3 Results and Discussion
### Testing of Drilling Core Samples
#### 3.2.3 Estimation of the Fibre Content
For the lower fibre content of 40 kg/m\({}^{3}\) large variations are visible with a minimum value of 25.1 kg/m\({}^{3}\) and a maximum value of 50.5 kg/m\({}^{3}\), which gives an accuracy of approximately \(\pm\)35% for single measurements, but a very accurate value for a number of nine specimens. In contrast, for the higher fibre content of 80 kg/m\({}^{3}\), the variations are smaller, with a minimum value of 67.8 kg/m\({}^{3}\) and a maximum value of 88.0 kg/m\({}^{3}\). This results in an accuracy of \(\pm\)15% for single specimens. Effects of the positions of the specimens inside the concrete plates, for example, specimen 5 in the middle and specimens 7 to 9 with low edge distances, can only marginally be detected, although the specimens 7 to 9 of both concre  
Figure 15: Calculated fibre content of the drilling cores depending on the fibre content by use of the adjusted fibre aspect ratio. calculated fibre content, what could be a hint for fibre accumulations near the edges, which lead to a higher fibre content near the corners. However, the small number of samples and especially the inconsistent results for the other specimens makes it hard to decide if this observation is just a random phenomenon or if the position of the specimen really has an influence on the fibre content of the specimens. One problem for future analysis is that either there must be an expected value for the fibre content to calculate the adjusted aspect ratio, or the specimen dimensions have to be chosen big enough to inhibit the problems with truncated fibres. Additionally, Equation (4) has to be reviewed with a large database of different SFRCs. After the revision of equation (4) based on an enlarged database, the test setup in combination with the FEM model will give the opportunity to estimate the fibre content of concrete structures by extracting drilling core samples, which only causes small damage to the whole structure.
# Research Trends in Collaborative Drones
## 7 Networked Drones Using Cellular Technologies
### Approach and Methodology  
The BVLOS concept has been explored with 4G cellular networks [12]. The 4G cellular networks made possible multimedia applications such as music and video streaming. The latency of 4G networks, in the order of 50 to 200 ms, does not address the real-time needs for drone command and control, which are in the order of a few milliseconds. Furthermore, 4G cellular networks have been designed for serving ground User Equipment (UE). In contrast, flying drones operate at altitudes. It has been observed that 4G cellular communications of flying drones are prone to high interference levels [12]. The resulting low Signal to Interference Ratio (SINR) values are causing degradation and loss of connectivity [2].  
Low network latency is a requirement for safe drone command and control. The 5G networks are aiming at network latency in the order of a few milliseconds. The 5G technology being deployed addresses the needs of drone communications. BVLOS is a use case of 5G networks [54; 55; 56]. BVLOS communications require reliable connections between pilots and drones for aspects such as command and control, telemetry and first-person camera video [57; 58]. The 5G network BVLOS communications build upon the Ultra Reliable Low Latency Communication (URLLC) use case [59]. It aims at a latency of a few milliseconds. Another goal is reliability, that is, less than a \(10^{-5}\) packet drop rate and less than \(10^{-9}\) bit error rate. Pilots can safely operate drones unhindered within these parameters by network traffic load and latency.
## 5 Calculation of Load Carrying Capacity
### Comparison of One-Sided Corrosion and Two-Sided Corrosion  
Because of the different parameters such as the slenderness ratio and the diameter-to-thickness ratio of specimens in this study and the study in [24], it is not possible to directly use these data to compare the difference between one-sided corrosion and two-sided corrosion. The results of the study in [24] are also shown in Figure 14, and it can be seen that the section reduction method can also be used to calculate the load carrying capacities of two-sided corrosion specimens. With the same corrosion ratio, the load carrying capacities of one-sided corrosion and two-sided corrosion specimens are shown in Figure 15, where \(N_{\text{co}}\) and \(N_{\text{ct}}\) are the load carrying capacities of the specimens calculated  
Figure 14: Comparison of the tested results and load carrying capacity predicted by using the section reduction method: (**a**) GB50017 – 2017; (**b**) EN 1993-1-1; and (**c**) ANSI/AISC 360-16.  
by considering one-sided corrosion and two-sided corrosion, respectively. It can be seen that \(N_{\text{ct}}\) is slightly larger than \(N_{\text{co}}\), but the results are basically the same.  
To study the difference between the two methods of one-sided corrosion and two-sided corrosion, the load carrying capacities of specimens were calculated by the above three specifications according to the previous values of corrosion ratio, eccentricity, slenderness ratio, and diameter-to-thickness ratio, as shown in Figures 16 and 17. It can be found that, with the increase of slenderness ratio, the calculated results of one-sided corrosion and two-sided corrosion are more and more different. Within the common range of engineering, the difference between the two corrosion methods decreases with the increase of diameter-to-thickness ratio. In general, the load carrying capacities of specimens calculated by the two corrosion methods are not much different. When the corrosion degrees of specimens do not exceed 20%, the results between the two corrosion methods are no more than 3%. It shows that different uniform corrosion modes have little influence on the calculation results of load carrying capacities of specimens. Two-sided corrosion can only be considered to accelerate the corrosion ratio in the accelerated corrosion test.  
Figure 15: Comparison of the load carrying capacity of the specimens with one-sided corrosion and with two-sided corrosion: (**a**) GB50017 – 2017; (**b**) EN 1993-1-1; and (**c**) ANSI/AASC 360-16.  
Figure 16: Relationship between \(\delta\) and \(\lambda\): (**a**) GB50017-2017; (**b**) EN 1993-1-1; and (**c**) ANSI/AASC 360-16.