esa-sceva/satcom-chunk-collection huggingface dataset:
# Synergistic Potential of Optical and Radar Remote Sensing for Snow Cover Monitoring
## 3 Data and Methods
### Data
#### 3.2.2 Snow Data  
The optical snow cover satellite dataset selected for this study was the MODIS Terra Snow Cover Collection 6 product [69]. This dataset offers long-term insights into the Normalized Difference Snow Index (NDSI), represented as a percentage ranging from 0 to 100%, alongside binary delineations for various screened-out areas such as cloud cover. It offers moderate spatial resolution (500 m) and a high temporal resolution (daily). The NDSI is a dimensionless index that ranges from \(-1\) to 1, but, in this dataset, negative NDSI values have been set to 0 (for a more detailed description of this index, see Appendix A). In this research, we used snow cover records spanning the period from 1 March 2000 until 31 May 2023.  
The Copernicus HRS&I SWS product provides information about wet snow and various features prone to interference with the radar backscattering signal, such as shadow, layover or foreshortening, forest areas, urban infrastructures, and water bodies. This dataset is available for specific mountainous regions of the European continent (the Alps, Eastern Turkey, Iceland, the Pyrenees, and Scandinavia) with a high spatial resolution of 60 m every 6 days. In this research, we focused on the Pyrenees (the only mountain range available in the Iberian Peninsula) and utilized the HRS&I SWS data from both ascending and descending orbits for the period from 1 November 2016 to 31 May 2023.
## 3 Results and Analysis  
Based on the swHPFM framework, the axial compressor rotor algorithm was implemented to evaluate the performance of the framework. First, the 2340-core-scale program, which simulates one blade channel per process and communicates between each process via MPI, was evaluated. Then, each blade channel was divided into 12 subdomains, with each process simulating a subdomain, and the 28080-core-scale program was implemented. In addition, the accuracy of the numerical simulation in this experiment was determined by using the numerical algorithm. Therefore, the residuals were platform-independent. To ensure the accuracy of the experimental results, each job was submitted 10 times, and the average of these tests was taken as the final result.
# Two-Dimensional Histogram Shifting-Based Reversible Data Hiding for H.264/AVC Video
## 2 HS-based RDH Technique
### Proposed 2D HS-Based RDH Schemes
#### 3.2.1 2D HS Using the \((0,0)\) Coefficient Pairs
Since the embedding efficiency represents the embedding capacity per unit modification, high embedding efficiency means that under the same payload, the amount of modification will be smaller, which will have less impact on video quality and file size. A video clip called 'bus' from Xiph.org ([[https://media.xiph.org/video/derf/](https://media.xiph.org/video/derf/)]([https://media.xiph.org/video/derf/](https://media.xiph.org/video/derf/))) is used to illustrate calculation of \(OE\). The first 90 frames of this video is encoded using the H.264/AVC codec with a quantization parameter (QP) of 16, and a group of pictures (GOP) structure IPBPBPBPBPBPBP. The number of each DCT coefficient pair with both values in the range \([-4,4]\) in the first GOP of video 'bus' is summarized in Table 1.  
Let \(OE_{o}\) and \(OE_{z}\) denote the overall embedding efficiency of our method and the method presented in [34], respectively. Based on the statistical results given in Table 1, \(OE_{o}\) and \(OE_{z}\) can be estimated as below. \[OE_{o} =\frac{\frac{251,897}{413,725}\times 2.25+\frac{127,853}{413,725} \times 1+\frac{339,975}{413,725}\times 0}{\frac{251,897}{413,725}\times 0.75+ \frac{127,853}{413,725}\times 1+\frac{33,975}{413,725}\times 1}{\frac{33 13,725}{413,725}\times 0.75}=1.980.\] \[OE_{z} =\frac{\frac{251,897}{413,725}\times 2.25+\frac{64,392}{413,725} \times 1.5+\frac{48,604}{413,725}\times 1+\frac{626}{413,725}\times 1+\frac{41,906}{413,725}\times 0}{\frac{251,897}{413,725}\times 0.75+\frac{64,392}{413,725} \times 1+\frac{48,604}{413,725}\times 1.5+\frac{6926}{413,725}\times 1+ \frac{41,906}{413,725}\times 2}{\frac{718,886.25}{416,958.75}}=1.724.\]  
It can be seen from the above calculation results that our overall embedding efficiency is higher than that of the method proposed in [34]. The reason is that the embedding modes used in [34] for the points on the coordinate axis affect the shift of those points without capacity gain. In [34], both values of those points that are shifted without data embedding need to be modified, while only one value will be modified in our scheme. When the video content is more complex and the compression rate is lower, the number of shifting-only points will increase, thus, the impact of bigger modifications will be more obvious.
# Enhancing Space Management through Digital Twin: A Case Study of the Lazio Region Headquarters
## 1 Introduction  
In the era of digital innovation, with the increasing need to adapt to the changing dynamics of building production, Digital Twin (DT) is becoming a critically important resource [1]. DT has achieved remarkable attention and is being increasingly adopted in a wide range of sectors, including manufacturing, energy, transport, healthcare, and others [2, 3]. By definition, a DT is a digital representation of a physical object, system, or process that is updated in real time with data from the corresponding physical object. This enables detailed monitoring and simulation of the operation and performance of the physical object, which is essential for optimizing the design and operation of complex systems [4]. In the Architecture, Engineering, Construction and Operations (AECO) sector, the adoption of DT is slower than in other sectors [5, 6]. This delay can be attributed to several factors peculiar to this sector that have made the implementation of innovation more challenging. Firstly, the complexity and diversity of construction projects means that they are perceived as singularities and, as such, require a tailored approach to the adoption of digital systems. Each project may present unique characteristics, including the type of building and the geography of the location. This makes it challenging to establish general standardization. Furthermore, the construction industry is characterized by considerable fragmentation, with multiple actors involved at different levels of the design, construction, and management process, making the coordination and sharing of data necessary for the effective implementation of shared data environments more complex [7]. Practitioners have been using traditional methods and tools for years and may be reluctant to adopt newtechnologies and processes, leading to widespread resistance to change in the sector. To overcome these challenges and enable the AECO sector to take full advantage of innovative digital technologies, targeted actions are needed, such as the standardization of data formats and communication protocols, which is essential to ensure that the different systems and software used in the industry can interoperate efficiently [8; 9]; the training of personnel, which is crucial to ensure that insiders are able to make the best use of new technologies and fully understand the potential of new intelligent systems; and the development of collaborative business models and processes that encourage data sharing and collaboration between all actors involved in the lifecycle of a construction project.
## 3 Experimental Validation
### Results
#### 3.2.2 Varying Voltage Magnitude  
In addition to the previously exposed harmonic response, the second experimental investigation aimed at assessing the effect of a non-sinusoidal excitation, by introducing a vanishing envelope to the driving voltage (Figure 11). Considering a center frequency of 1 Hz, results are shown in Figure 12, both in terms of voltage-strain curve and strain time-domain waveforms. Again, good agreement between experimental measurements and model prediction can be observed, with a slight overestimation of the strain when considering the theoretical analysis. Still, the model quite accurately relates the hysteretic effect of the actuator, including minor loops. It can be noted that while the sole quasi-static model would yield minor loops sticking to the main one, the introduction of time-dependent fractional derivatives allows predicting detachment of such minor loops from the main path, as well as smooth strain rate reversal.  
Nevertheless, one of the major assets of the enhancement provided by this study is the ability to relate well the strain offset that appears. In particular, time-domain waveforms in Figure 12b show that the proposed approach permits assessing the change in such a strain shift as the voltage magnitude decreases. Moreover, it can be noted that, even when the driving voltage keeps zero value,  
Figure 10: Experimental and predicted loop areas in steady state and strain shifts.  
strain continues to decrease (time longer than 5.5 s), which is also very well predicted by the model as shown in the enlargement in Figure 12b.
###### Abstract  
The aim of the study was to select microbiological inoculants for a specific plant species, i.e., white lupine (_Lupinus albus_ L.), to increase the efficiency of the diazotroph process. The research involved an in vitro assessment of interactions between the symbiotic bacteria (_Bradyrhizobium_ sp. isolated from Nitragina and Nitroflora commercial preparations dedicated to white lupine) and selected endophytes (_Pseudomonas fluorescens_ or _Bacillus subtilis_) used for seed coincocation. In addition, selected morphological traits of plants (the weight and length of aboveground and belowground parts) were examined after the inoculation/coinoculation. The degree of root colonisation by selected endophytes used as individual inoculants and in combination with bacteria of the _Bradyrhizobium_ genus was determined. The diazotrophic parameters were also investigated (nitrogenase activity, the number, and weight of nodules). The results showed no antagonistic interactions have been demonstrated between bacterial strains of the genus _Bradyrhizobium_ sp. isolated from Nitragina and Nitroflora, and the endophytes _Pseudomonas fluorescens_ or _Bacillus subtilis_ used for the study. The applied coincocation in vitro had a stimulating effect on the weight of the stems and roots of white lupine causing an average increase of 13% and 28%, respectively. The level of nitrogenase activity in the coincocation variants increased from 3.5 nMC\({}_{2}\)H\({}_{4}\) plant\({}^{-1}\) h\({}^{-1}\) to an average of 32.34 nMC\({}_{2}\)H\({}_{4}\) plant\({}^{-1}\) h\({}^{-1}\).  
Pesudomonas fluorescens; Bacillus subtilis; Bradyrhizobium sp.; diazotrophy; nodules; plants parameters +
Footnote †: journal: Applied Sciences
# Unequal Interval Dynamic Traffic Flow Prediction with Singular Point Detection
## 5 Multiscale Traffic Flow Prediction via Vasicek Model
### Unequal Interval Dynamic Traffic Flow Prediction Design Based on Vasicek Model
The relative mean standard error (RMSE) between the increment of the traffic flow in the \(i\)th interval \(\frac{\triangle_{j}X}{\triangle_{k}t}\) and the sampled increments of the observation from the \(1\)st to \(i\)th interval is used as the metric of the prediction model. The sum of the RMSE [26] will be minimized based on the weighted linear function to determine the value of the parameters as  
\[\min_{\alpha_{1}(k),\alpha_{0}(k)}\sum_{j=1}^{k}\{\frac{\triangle_{j}X}{ \triangle_{j}t}-[\alpha_{0}(k)+\alpha_{1}(k)X_{j}]\}^{2}K_{h}(\frac{t_{j}-t_{ k}}{h}), \tag{19}\]  
where \(\frac{\triangle_{j}X}{\triangle_{j}t}\) denotes the predicted value of the traffic flow's increments in the \(j\)th interval. \(K_{h}(\frac{t_{j}-t_{k}}{h})\) denotes the weighted linear function, which can be described as  
\[K_{h}(t)= \begin{cases}\frac{3}{4h}(1-t^{2}),&-1\leq t<0\\ 0,&t<-1,t\geq 0.\end{cases}  \tag{20}\]  
Here, \(h\) denotes the bandwidth parameter of the one-sided Epanechnikov. Based on the algorithm [34], the optimization problem can be solved, and the parameter \(\alpha\) can be obtained by  
\[\alpha=(X^{T}WX)^{-1}X^{T}WY, \tag{21}\]  
where \(W\), \(Y\), \(\alpha\), and \(X\) can be described as  
\[W=diag[K_{h}(t_{1}-t_{k}),K_{h}(t_{2}-t_{k}),\ldots,K_{h}(t_{i}-t_{k})], \tag{22}\]  
\[Y=[\frac{\triangle_{1}X}{\triangle_{1}t},\frac{\triangle_{2}X}{\triangle_{2}t },\ldots,\frac{\triangle_{i}X}{\triangle_{i}t}], \tag{23}\]  
\[\alpha=[\alpha_{0}(k),\alpha_{1}(k)]^{T}, \tag{24}\]  
and  
\[X=[X_{1},X_{2},\cdots,X_{i}]. \tag{25}\]  
Based on the determined value of the parameters, the increment of the traffic flow in the \(i\)th interval \(\triangle_{i}X\) can be obtained. The traffic flow trend before the next predicted singular point can be output.
# Dichotomization of Multilevel Variables to Detect Hidden Associations
## 2 Preliminaries  
### Contingency Tables  
In simple words, a contingency table is a two-way layout of the number of items in the sample set that fall into different categories [13; 14]. It is a cross-classification table in which the association between the variables (or if they are not related to each other) can be seen.  
A two-way contingency table is created from a sample by using two qualitative variables (henceforth, we will refer them as V\({}_{1}\) and V\({}_{2}\), respectively). The number of columns corresponds to the levels (distinct values) of V\({}_{1}\) (\(m\)), and the number of rows corresponds the levels of V\({}_{2}\) (\(n\)). Therefore, the number of entries or cells is \(m\times n\).  
Table 1 shows an example of a two-way contingency table for two dichotomous variables V\({}_{1}\) and V\({}_{2}\). The levels of V\({}_{1}\) are C\({}_{1}\), C\({}_{2}\), whereas for V\({}_{2}\) they are B\({}_{1}\), B\({}_{2}\). The content of each cell is computed from the sample as follows:  
\(a\): The number of elements that simultaneously belong to categories B\({}_{1}\) and C\({}_{2}\).  
\(b\): The number of elements that simultaneously belong to categories B\({}_{2}\) and C\({}_{1}\).  
\(c\): The number of elements that simultaneously belong to categories B\({}_{1}\) and C\({}_{2}\).  
\(d\): The number of elements that simultaneously belong to categories B\({}_{2}\) and C\({}_{2}\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**V\({}_{1}\)**} & \multicolumn{2}{c}{**V\({}_{2}\)**} & \multirow{2}{*}{**Total**} \\ \cline{2-2} \cline{4-4}  & **C\({}_{1}\)** & & **C\({}_{2}\)** \\ \hline B\({}_{1}\) & \(a\) & \(c\) & \(a+c\) \\ B\({}_{2}\) & \(b\) & \(d\) & \(b+d\) \\ \cline{2-2} Total & \(a+b\) & \(c+d\) & N \\ \hline \hline \end{tabular}
\end{table}
Table 1: Example of a \(2\times 2\) contingency table.  
The letters \(a,b,c\) and \(d\) are the observed frequencies. The sum of rows and columns of a contingency table is called the marginal totals. The values \(a+c\) and \(b+d\) form the marginal rows. The values \(a+b\) and \(c+d\) are the marginal columns. N is the size of the sample. The degrees of freedom are computed with \(df=(m-1)\times(n-1)\); therefore, for the example of Table 1, \(df=1\).  
Once a two-way contingency table is created, a test of independence can be applied to discover an association between the variables V\({}_{1}\) and V\({}_{2}\). In the next Subsections, we will explain two of the most common tests of independence.
# Gaussian Dynamic Convolution for Semantic Segmentation in Remote Sensing Images
## 4 Experiments
### Ablation Study  
We explore the impact of different types and numbers of convolutional layers in the pyramid pooling module on the segmentation results in Table 3. We first set the number of intermediate convolutional layers to 1, and compare the results obtained using dilated convolution (with a dilation factor of 6), deformable convolution, and Gaussian dynamic convolution (to ensure fairness, the base offset of GDConv is also set to 6 and \(\sum\) to 2). Our GDConv module is going to improve 1.3 mIoU over the dilated convolution and 4.1 mIoU over the deformable convolution. Then, we set the number of intermediate convolution layers to 2, where the dilation factors of the two dilated convolutions are set to 6 and 12 (reverting to ASPP modules). To ensure fairness, the base offsets of GDConv are also set to 6 and 12, and \(\sum\) remains 2. The results show that our GPP structure is 2.2 mIoU better than the original ASPP module and 5.2 mIoU better than the two deformable convolutions, which fully demonstrates the superiority of Gaussian dynamic convolution in handling remote sensing image segmentation tasks.  
In Table 4, a set of experiments was performed in order to investigate how the standard deviation \(\sum\) would affect the final segmentation results. The value of the standard deviation \(\sum\) indicates the size of the range that affects the receptive field. From Tables 3 and 4, it can be seen that Our GDConv produces better results than the two compared convolutions for different values of \(\sum\). In particular, the best results are obtained when \(\sum\) is taken as 2.0. We can see that either large or small \(\sum\) causes performance degradation. The reason is that the large \(\sum\) may fuse unnecessary noise information, and the small \(\sum\) leads to insufficient sampling area to fully acquire the desired features.  
Figure 6: More visualization results on the iSAID val set.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **mIoU(\%)** \\ \hline \(1\times\) Dilated Conv (dilation = 6) & 58.4 \\ \(1\times\) Deformable Conv & 56.6 \\ \(1\times\) GDConv (base = 6, \(\sum\) = 2) & 60.7 \\ \(2\times\) Dilated Conv (dilation = 6, 12) & 61.3 \\ \(2\times\) Deformable Conv & 59.3 \\ \(2\times\) GDConv (base = 6, 12, \(\sum\) = 2) & 64.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Segmentation performance between GDConv and other convolutions on iSAID val set.
## 4 Experimental Verification
### Tracking Performance
When a \(100\) Hz signal is input to excite the FSM, the open-loop system exhibits a phase lag of \(14.4^{\circ}\), the rate correction term introduces a phase lead of \(61^{\circ}\), and the filter itself contributes a phase lag of \(39^{\circ}\), resulting in a phase lag of \(0.69^{\circ}\) for the closed-loop control system. The closed-loop frequency-response curve for the proposed composite control method is shown in Figure 18. It can be observed that the introduction of the filter has little effect on the amplitude and phase of the \(100\) Hz signal. Before cascading the filter, the peak value of the amplitude-frequency characteristic curve is \(5.22\) dB. After introducing the filter, this peak value is decreased to \(2.94\) dB. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Types of** & **Traditional Composite Control** & **Proposed Composite Control** \\
**Reference Trajectories** & **(NME/NRMSE)** & **(NME/NRMSE)** \\ \hline Single-frequency signal & 2.43\%/1.59\% & 0.52\%/0.22\% \\ Decreasing signal & 2.56\%/1.47\% & 0.97\%/0.58\% \\ Multi-frequency signal & 1.87\%/1.02\% & 0.96\%/0.58\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Tracking results of different control methods. Figure 16: Compensation results of proposed composite control method and traditional composite control method for multi-frequency input signal: (**a**) tracking curves; (**b**) tracking errors.
###### Abstract
Instead, GLIE fluctuations monitor the general chance of all image pixels to experience changes in gray levels due to particles motion. Evaluation of cell dynamics through GLIE fluctuation analysis enabled differentiation of live from dead cells [16] and exploration of a range of cellular physiological conditions that included malignancy [8; 15]. Complex intracellular dynamics often result in anomalous diffusion [12; 17; 18]. Brownian diffusion and multiple types of anomalous diffusion can be related by analytical models to a Power Spectral Density (PSD) description of motion that obeys the following general formula: _Power_\((f,\infty)=\frac{D}{f^{\beta}}\), where \(f\) represents frequency, \(\beta\) relates to the power of diffusion and \(D\) represents a constant that is correlated to the diffusion coefficient (\(K_{\alpha}\)) [19; 20; 21]. PSD (or Amplitude Spectral Density (ASD)) analysis of motion with its fitted power formula enables the determination of specific motion characteristics, including the diffusion coefficient, the power of diffusion and a break in ergodicity [19; 20; 21]. Here, we aim to combine the detection and characterization of intracellular motion by monitoring GLIE changes of the inspected medium with the PSD analysis of these changes and their power-fits. In that way, we wish to combine the sensitivity of GLIE changes in the detection of intracellular motion with the efficiency of PSD analysis in resolving this motion. We first study theoretically the sensitivity of our analysis of pixels GLIE through simple examples of particle motion. Next, we test experimentally, in living cells, the response of GLIE fluctuation parameters to a state of reduced intracellular dynamics due to mitochondrial dysfunction and ATP depletion. Finally, we explore the possible use of GLIE fluctuation parameters to differentiate malignant lymphocytes (Jurkat cells) from benign lymphocytes. Previous studies have used information embedded in pixel intensities from static images of fixed cells [22; 23; 24], fixed tissues [25] or from radiological images [26]. In contrast, our method uses time-dependent pixels data. Focusing on fluctuations in such dynamic data may provide more direct insight into the cell dynamics and mechanical work. We expect that the simplicity of our approach and its \"label free\" nature will make it an important new tool for studying multiple physiological conditions in cells, such as mitochondrial dysfunction and malignancy.
# Evolutionary Reinforcement Learning: A Systematic Review and Future Directions
## 6 Open Issues and Future Directions
### Open Issues  
#### 6.1.1 Scalability to High-Dimensional Spaces  
The challenge lies in extending EvoRL methodologies to effectively handle the complex, high-dimensional action and state spaces commonly encountered in real-world applications, such as autonomous vehicles [100], unmanned aerial vehicles [101], and large-scale industrial systems. Overcoming this hurdle entails the development of EvoRL algorithms capable of efficiently exploring and exploiting these expansive spaces while maintaining computational tractability. Furthermore, ensuring the scalability of EvoRL necessitates the implementation of innovative techniques to handle the curse of dimensionality, facilitate effective knowledge transfer [102] across related tasks, and enable the discovery of meaningful solutions amidst the inherent complexity of high-dimensional environments.