esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
### Variations of Chlorophyll Fluorescence across Scales  
In our study, the diurnal changes of the canopy \(\mathrm{SIF}_{687}\) and \(\mathrm{SIF}_{760}\) both showed typical single-peaks (Figure 5a,b), similar with the pattern of the irradiance incident above the canopy (Figure 5d).The variation in canopy \(\mathrm{SIF}\) was dominated by the incident PAR above the canopy [42, 43] and even more correlated to the absorbed PAR than GPP at short-time resolution [44]. In contrast, the variation of leaf \(\mathrm{F}_{\mathrm{s}}\) was non-obvious during the daytime (Figure 6a). Although the changes of leaf \(\mathrm{F}_{\mathrm{s}}\) are also related to the irradiance [45, 46], the correlation was weakened in this study because of the average of \(\mathrm{F}_{\mathrm{s}}\) across leaves under different illumination due to the canopy geometry and varied solar irradiance. The incident irradiance field is required to be carefully characterized when we compare spectra-based \(\mathrm{SIF}\) observations with PAM fluorescence measurements. The values and diurnal variations of \(\mathrm{PAR}_{\mathrm{leaf}}\) were both less than that of \(\mathrm{PAR}_{\mathrm{canopy}}\) in our results (Figures 5d and 6d). Another explanation might be the influence of different fluorescence emission bands for canopy \(\mathrm{SIF}\) and \(\mathrm{F}_{\mathrm{s}}\). \(\mathrm{SIF}\) was usually measured at narrow wavelengths of O\({}_{2}\)-A (687 nm) and O\({}_{2}\)-B (760 nm) at canopy and landscape levels, whereas leaf \(\mathrm{F}_{\mathrm{s}}\) was measured over a broad spectral range between 700 and 715 nm. \(\mathrm{ChIF}\) signal emitted from plants contains two crests in the range of 650-800 nm: One in the red spectral region (650-700 nm) with a maximum around 687 nm is mainly contributed by photosystem II (PSII) and the other in the near-infrared spectral region (700-800 nm) with a maximum near 740 nm is contributed by both photosystem I (PSI) and PSII [2]. Some model-based studies found the different sensitivities of \(\mathrm{SIF}\) bands to leaf biochemistry and canopy structure [22, 47].  
Canopy \(\mathrm{SIF}\) signal changed throughout the season. Canopy structure and leaf pigments matter on the variation of canopy \(\mathrm{SIF}\) on the seasonal scale except for the illumination effect. Despite no sizeable seasonal variation of LAI, it displayed a slight increase in summer, which might lead to a rise in \(\mathrm{SIF}\) emitted by total leaves. Leaf chlorophyll content modulates light absorption and fluorescence reabsorption, thereby regulating \(\mathrm{ChIF}\). Leaf chlorophyll content usually varies within the seasonal range, which is especially obvious due to leaf development and senescence [48, 49]. However, the adjustments in chlorophyll content rarely occurred in these subtropical evergreen needles during the seasons. However, the retrieved carotenoid content varied a large range from 2.86 to 13.14 \(\mathrm{\SIUnitSymbolMicro g}\) cm\({}^{-2}\). In addition, the effects of xanthophyll pigment also need to be taken into consideration to comprehend the seasonal variability of \(\mathrm{SIF}\) for evergreen forests [5].
# We Do Not Anthropomorphize a Robot Based Only on Its Cover: Context Matters too!
## 4 Anthropomorphizing Factors: Robotic Factors Are Not Enough
### Human Factors Also Depend on the Users Themselves, Not Just the Robots
#### 4.3.5 But There Is More  
Other user factors, such as previous experience with technology, education, expectations of robots, social isolation, and developmental type can impact robot acceptance.  
Users who are more experienced with new technologies (computer training and/or knowledge of voice recognition devices) rate the social skills of two robots (OLIVIA and CYNTHIA) significantly lower than inexperienced users. This result suggests that more experienced users tend to be less open to perceiving robots as social entities [112]. Moreover, 4-7 y.o. with little or no experience with robots assign more psychological characteristics to them than experienced children [199]. Moreover, the more educated an individual is, the less likely they are to perceive the robot as a social entity [200].  
Individuals judge a robot (KAROTZ) more positively before having met it versus after the interaction, which shows that, on the one hand, they generally have high expectations toward robots, and on the other hand, these expectations were not met when they met the robot [154]. Low initial expectations lead to less disappointment during the interaction [33]. In a long-term interaction, the people who had the highest expectations toward the robot before the interaction show an abandonment rate of the procedure that is higher than individuals with lower initial expectations [154].  
In addition, isolated individuals would evaluate interactions with a robot (APRIL) more positively and rate the robot more attractive than socially well-connected individuals [47]. Moreover, loneliness would lead to more anthropomorphic attributions toward an animal or a technological gadget [46]. This increased tendency toward anthropomorphism can vary according to the robot's appearance since socially isolated people attribute more human characteristics to a robot that looks like a human than to a robot that looks like an animal [48]. As we have seen before, it could also depend on the user's culture, as a reverse pattern was observed among Chinese participants with lonely individuals anthropomorphizing less [182].  
The developmental type can also modulate robot perception and interaction. For example, individuals with autism spectrum disorder (ASD) would not show a human preference bias, unlike neurotypical individuals who have more affinity with another human being than with an artificial object [201; 211]; and they consider a human voice and a robotic voice to be similar [110]. They would also have more difficulty interpreting the robot's mental states than typically developing children [180].
## 9 Efficiency and Scalability  
For efficiency and scalability evaluation, it is important to note that the overhead imposed by our method will occur only when the routing tables have to be updated; this means at network initialization and whenever events such as administrative decisions or event-driven policies cause network reconfigurations; otherwise, for normal operation, the network will run as any SDN network.  
As we have seen, labels can be assigned by administrators or may be calculated from _Channel_ or _CF_ relations, which may mean capability lists or access control matrices. In [21], [PERSON] and [PERSON] presented a method for calculating labels based on such information. They showed that a worst-case estimate of label calculation time is for an algorithmic complexity that is cubic on the number of entities in the network (thus excluding exponential complexity). MATLAB simulations yielding estimates were also given in that paper. It was shown in those simulations that for a network of 10,000 entities, the partial order can be found and the labels calculated in about 1.5 min, raising to about 10 min for 20,000 entities, and after that rising rapidly to 1.75 h for 100,000 entities.  
These times can improve with more efficient programs and faster computers. Research on efficient graph computations is continuously progressing. Consider also that many IoT networks can be partitioned into partially independent _slices_, as they are called in 5G or _domains_. In practice, many slices or domains can be smaller than the mentioned 10,000 entities, and reconfigurations may affect only some of them.  
If, on the other hand, a _CF_ relation must be calculated from labels, this also can be done efficiently by setting membership tests, using the relation _CF(x,y)_iff \(Lab(x)\subseteq Lab(y)\)[20]. Finally and most importantly, in many practical cases, policies and configurations are set up in such a way that global recalculations are unnecessary since only limited and already planned local changes will occur, with minimal overhead. Due to the many different contexts in which our method can be used, more detailed efficiency considerations, as well  
Figure 17: Simulation result of an authorized flow.  
Figure 18: Simulation result of an unauthorized flow.  
as the adaptation of the method to each context, are left to future research. As a practical example, we may think of a network with 10,000 entities that need to be updated once a day, leading to an overhead of 1.5 min a day.
# Abnormal Activity Detection Using Pyroelectric Infrared Sensors
## 7 Experimental Evaluation
### Evaluation Metrics  
The performance of the abnormal human-activity detection methods can be evaluated in terms of two rates, detection rate and false alarm rate. The detection rate is computed as the ratio of the number of correctly detected abnormal activities to the total number of abnormal activities. The false alarm rate is computed as the ration of the number of normal activities that are incorrectly detected as abnormal activities to the total number of normal activities.  
Based on the confusion matrix shown in Table 1, the two metrics can be defined as follows:  
\[\text{Detection Rate} =\frac{TP}{TP+FN} \tag{18}\] \[\text{False Alarm Rate} =\frac{FP}{FP+TN} \tag{19}\]  
The performance of an ideal abnormal human-activity detection algorithm should have a high detection rate and a low false alarm rate. Therefore, we evaluate the performance of the algorithms using an Receiver Operating Characteristic (ROC) curve, which plots the detection rate against the false alarm rate. In addition, we compute the area under the ROC curve (AUC) to compare these algorithms. AUC is a better measurement than accuracy in the evaluation of learning algorithms [37,38], especially in the cost-sensitive problems. A desirable algorithm with a high detection rate and a low false alarm rate should have an AUC value closer to one.
# TA-RRT*: Adaptive Sampling-Based Path Planning Using Terrain Analysis
###### Abstract  
This paper proposes a novel algorithm, Terrain Analysis-Rapidly exploring Random Tree* (TA-RRT*), which improves the performance of path planning using adaptive sampling and step size based on terrain analysis. While existing RRT*-based algorithms perform node sampling and tree expansion in various ways to optimize path planning, they may still generate inefficient paths in complex terrain environments. In this paper, TA-RRT* analyzes the complexity of the terrain to generate a probability density function, which is then used to guide adaptive sampling and tree expansion during the path planning process. In addition, we reduce the length of generated paths using an adaptive step size based on terrain complexity. To verify the performance of the proposed algorithm, we conduct experiments with various path planning algorithms in three different environments: simple, intermediate, and complex. The experimental results demonstrate that TA-RRT* outperforms other algorithms in terms of path length, computational time, and memory usage. Furthermore, its robustness was validated in dynamic environments, where it effectively performed real-time path replanning to adapt to environmental changes, such as the appearance of new obstacles.  
Keywords:path planning; RRT*; terrain analysis; adaptive sampling; adaptive step size
# SAR Interferogram Filtering of Shearlet Domain Based on Interferometric Phase Statistics
###### Abstract  
This paper presents a new filtering approach for Synthetic Aperture Radar (SAR) interferometric phase noise reduction in the shearlet domain, depending on the coherent statistical characteristics. Shearlets provide a multidirectional and multiscale decomposition that have advantages over wavelet filtering methods when dealing with noisy phase fringes. Phase noise in SAR interferograms is directly related to the interferometric coherence and the look number of the interferogram. Therefore, an optimal interferogram filter should incorporate information from both of them. The proposed method combines the phase noise standard deviation with the shearlet transform. Experimental results show that the proposed method can reduce the interferogram noise while maintaining the spatial resolution, especially in areas with low coherence.  
phase filtering; phase statistics; shearlet; coherence; Interferometric SAR (InSAR) 2017 141 10.3390/app7020141
## 2 Methods  
### Main Components of the Method  
Initially, SAR data encompassing the designated study area along with an external Digital Elevation Model (DEM) are gathered, and the data set is processed through registration and difference. Subsequently, by employing the phase gradient calculation formula, the phase gradient is computed and a stacking process is executed to yield the phase gradient stacking outcome. This outcome integrates the phase gradient in each direction, culminating in the fusion of these directional phase gradients. The landslide detection methodology, delineated in three primary stages (as depicted in Figure 1), unfolds as follows:  
1. Data Collection and Pre-processing: SAR imagery and the DEM are acquired, with subsequent geocoding of the primary image to simulate the topographic phase under SAR coordinates using the DEM. Interferometric pairs are selected based on predefined spatial and temporal baseline thresholds, following a construction approach akin to the Small Baseline Subset (SBAS), resulting in acquisition of differential interferograms composed of small baseline sets. Processing was performed in the GAMMA202202 software.
2. Multi-direction phase Gradient Stacking and Fusion: the phase gradient calculation method is applied to calculate the phase gradients in the four directions of the interferograms, and then the phase gradients in the four directions of the interferograms are stacked with the temporal baseline as the weighting factor. The noise is removed by means of spatial filtering, and the stacked results of the phase gradients are fused in the four directions, and the fused stacked results of the phase gradients are obtained.
3. Landslide detection: leveraging the fused multi-direction phase gradient stacking results, regions exhibiting outliers are identified. These outliers predominantly arise from deformation-induced anomalies, where the interference pattern stacking across the time series mitigates atmospheric influences. We consider these anomalous regions as target areas, especially regions that indicate landslides.  
Figure 1: Flowchart of the method.
## 3 Results
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model** & **Race** & **Class** & **FPR Pre-Adjust** & \begin{tabular}{c} **FPR** \\ **Post-Adjust** \\ \end{tabular} & \
\begin{tabular}{c} **TPR** \\ **Pre-Adjust** \\ \end{tabular} &
\begin{tabular}{c} **TPR** \\ **Post-Adjust** \\ \end{tabular} \\ \hline RegNet & non-White & 0 & 0.3577 & 0.0759 & 0.9375 & 0.1989 \\  & 1 & 0.1034 & 0.6321 & 0.0459 & 0.7160 \\  & 2 & 0.5598 & 0.2060 & 0.5000 & 0.2891 \\  & White & 0 & 0.4375 & 0.0759 & 0.5000 & 0.1989 \\  & 1 & 0.0738 & 0.6321 & 0.0549 & 0.7160 \\  & 2 & 0.4019 & 0.2060 & 0.5639 & 0.2981 \\ ResNet50 & non-White & 0 & 0.3462 & 0.3304 & 0.5625 & 0.4375 \\  & 1 & 0.5172 & 0.5402 & 0.5872 & 0.5489 \\  & 2 & 0.0769 & 0.1154 & 0.0238 & 0.1219 \\  & White & 0 & 0.3304 & 0.3304 & 0.4375 & 0.4375 \\  & 1 & 0.5772 & 0.5402 & 0.5824 & 0.5489 \\  & 2 & 0.0748 & 0.1154 & 0.0902 & 0.1219 \\ ConvNeXt & non-White & 0 & 0.2692 & 0.5265 & 0.8125 & 0.8034 \\  & 1 & 0.3966 & 0.4441 & 0.3899 & 0.4262 \\  & 2 & 0.2735 & 0.0312 & 0.5000 & 0.0068 \\  & White & 0 & 0.2098 & 0.5265 & 0.5625 & 0.8034 \\  & 1 & 0.4228 & 0.4441 & 0.4066 & 0.4262 \\  & 2 & 0.2243 & 0.0312 & 0.4511 & 0.0068 \\ ResNet152 & non-White & 0 & 0.0500 & 0.4895 & 0.0000 & 0.7500 \\  & 1 & 0.5000 & 0.4986 & 0.5046 & 0.4946 \\  & 2 & 0.4402 & 0.0000 & 0.5000 & 0.0000 \\  & White & 0 & 0.0357 & 0.4895 & 0.0000 & 0.7500 \\  & 1 & 0.5101 & 0.4986 & 0.5275 & 0.4946 \\  & 2 & 0.3832 & 0.0000 & 0.5038 & 0.0000 \\ ResNet101 & non-White & 0 & 0.2231 & 0.4420 & 0.1250 & 0.5005 \\  & 1 & 0.6552 & 0.2012 & 0.5596 & 0.2422 \\  & 2 & 0.2137 & 0.3196 & 0.1429 & 0.3292 \\  & White & 0 & 0.2545 & 0.4420 & 0.1250 & 0.5005 \\  & 1 & 0.4832 & 0.2012 & 0.4176 & 0.2422 \\  & 2 & 0.2897 & 0.3196 & 0.3008 & 0.3292 \\ EfficientNet & non-White & 0 & 0.2462 & 0.3849 & 0.6875 & 0.6581 \\  & 1 & 0.5172 & 0.2245 & 0.4404 & 0.3421 \\  & 2 & 0.2821 & 0.2646 & 0.2143 & 0.3787 \\  & White & 0 & 0.2009 & 0.3849 & 0.5000 & 0.6581 \\  & 1 & 0.5705 & 0.2245 & 0.3956 & 0.3421 \\  & 2 & 0.3084 & 0.2646 & 0.2481 & 0.3787 \\ ResNet18 & non-White & 0 & 0.0885 & 0.3341 & 0.0000 & 0.4074 \\  & 1 & 0.1034 & 0.5663 & 0.1835 & 0.5816 \\  & 2 & 0.7436 & 0.0812 & 0.7857 & 0.0952 \\  & White & 0 & 0.0670 & 0.3341 & 0.1250 & 0.4074 \\  & 1 & 0.1812 & 0.5663 & 0.1429 & 0.5816 \\  & 2 & 0.8131 & 0.0812 & 0.7218 & 0.0952 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Model performance before and after fairness adjustments in the multi-class classification setting.
## 4 Results and Discussion
### Ultimate Volumetric Water Content and Water Storage  
The changes in the volumetric water content allowed the determination of the water storage in the soil layers [3; 25; 32]. Observing the water storage of the cover over time during the tests reveals a correlation with the coarseness of the middle layer. Intriguingly, although the earliest breakthrough occurred in the section using fine gravel, the highest water storage was noted when gravel was employed in the middle layer, even before the breakthrough (Figure 6). Across all covers, a sharp increase in water storage was observed at the beginning of infiltration in the first layer. Subsequently, this increasing trend diminished for varying durations before rising again post-breakthrough, where the third layer began to store water.  
In addition, Figure 7 demonstrates an increment in the final volumetric water content of the top layer as the gradation of the middle layer increases. The middle layer's influence on the upper layer's volumetric water content is particularly noticeable near the interface. A breakthrough occurs when the water potential at the interface reaches the water entry value of the coarse layer. The coarser soil generally has a lower water entry value [23; 24; 25]. Therefore, water infiltration into coarser-grained soils (e.g., fine gravel) occurred after the upper layer reached a higher saturation level than in other experiments.  
Figure 6: The change in water storage of soil columns during infiltration tests.
# Model Retraining upon Concept Drift Detection in Network Traffic Big Data
## 5 Experimentation Including the Results and Discussion
### Deploying the Model  
As per Figure 4b, once the model has been trained and tested, to detect potential drift, small batches of new data are inserted. The amount of data inserted in each batch is presented in Table 11. Most models require large amounts of data, but Isolation Forest works well even when the small sizes are small [6]. In fact, contrary to general machine learning models, large amounts of data often reduces Isolation Forest's ability to isolate anomalies as normal instances can mask Isolation Forest's ability to isolate anomalies [6]; hence, small batch sizes of new data were used.  
The techniques presented in Figure 1 were randomly spread throughout the batches, simulating the sporadic nature of attacks. Very small batch sizes could lead to false positives, while overly large batches might delay drift detection. Each new data batch is evaluated against the trained model, and performance metrics are recorded. As can be seen from the results in Table 11, as new data is being added, the accuracy and other metrics, precision, recall, and F1-Score, are decreasing. Additionally, the rate of false positives and false negatives were analyzed using confusion matrices. With the first batch, the accuracy was at 92.49%. With the insertion of the second batch of data, the accuracy dropped to 89.73%, and so on. And finally, with the insertion of batch 10, the accuracy was at 73.72%. There was clearly a concept drift even with very small batch sizes. The smaller batch sizes also attest to the fact that our model is highly sensitive to drift, and stronger security systems should in fact have higher sensitivity to detecting drift. In real-world scenarios, attacks will often not come in large numbers.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \
\begin{tabular}{c} **Total** \\ **Records** \\ \end{tabular} & **Accuracy** & **Precision** & **Recall** & **F1-Score** &
\begin{tabular}{c} **Total** \\ **Time (s)** \\ \end{tabular} \\ \hline Training & 534,365 & 98.99\% & 98.99\% & 98.99\% & 98.99\% & \\ \hline Testing & 134,398 & 98.95\% & 98.95\% & 98.95\% & \\ \hline \hline \end{tabular}
\end{table}
Table 10: Model performance metrics: training vs. testing data (baseline results).
## 3 Materials and Methods
### Methods
#### 3.4.4 Density Map Regression Module  
As shown in Figure 4, \(F_{k}\) (\(k\) = 1,2, \(\ldots\), 5) represents the intermediate feature maps output from the top to the bottom of the MSFA module. These feature maps are based on the input of the current path. By densely connecting the feature maps of different stages of other paths, shallow spatial features and deep semantic features are integrated, thereby enhancing the network's ability to recognize objects of different scales. However, due to the possible information loss in the layer-by-layer feature extraction process and the possible introduction of noise during feature fusion, it is not ideal to perform regression tasks on only a certain intermediate feature map. Inspired by TEDnet [41] and SDN [42], we perform density map regression on the feature maps output by each path and introduce multiple losses between the prediction results and ground truth (GT) to achieve distributed supervision. This supervision method provides additional gradient signals for the network, shortens the gradient propagation path, and effectively alleviates the difficulty of updating parameters such as weights and biases. Since the intermediate output of each path integrates the information of other paths, it is of great significance to supervise each path.  
Specifically, the feature map output by a certain path will be further extracted through three convolutional blocks that reduce the number of channels layer by layer, and finally, the density map will be regressed through a \(1\times 1\) convolution. The convolutional block consists of a depth-wise separable convolution with a kernel size of 3 \(\times\) 3 and a ReLU activation layer. This design effectively reduces the number of parameters and computations while maintaining the feature extraction capability of ordinary convolution. Since the size of each intermediate output is halved in turn, we use bilinear interpolation to downsample the GT density map to the corresponding size to maintain consistency. During training, to adapt to the memory limitation and improve the generalization of the model, we crop the training set images at random positions with a fixed size of 256 \(\times\) 256. However, considering the small size of the intermediate output \(F_{5}\) of the last path, downsampling the GT map to the same size may result in a large information loss, resulting in the possibility of misleading information introduced by additional supervision. Therefore, we abandon the additional supervision of the last path to avoid negatively affecting the model performance.