esa-sceva/satcom-chunk-collection huggingface dataset:
# Secured VM Deployment in the Cloud: Benchmarking the Enhanced Simulation Model
## 6 Observations
### Probabilistic Assessment: Proposed Model vs. Traditionally Used Models  
Each algorithm undergoes execution 10 times to obtain the figures of the overall integrity that need to be decreased to guarantee an equitable comparison. The quantitative consequence for different counts of VMs allocated to one PM for performing the task scheduling function is displayed in Table 6. On observing the mean value for 10 counts of VMs, the presented work has the best lowest value of 1786.4, whereas the existing models have higher mean values of ABC + BA = 1996.3, ACO = 2042.5, CSA = 1985.3, KH = 2754.1, WOGA = 2110.5, and ILWOA = 2110.7. When 20 counts of VM are allocated to 1 PM, the best value of the presented model is 3.23%, 8.72%, 10.09%, 27.82%, 6.62%, and 9.61% better than existing ABC + BA, ACO, CSA, KH, WOGA, and ILWOA methods. Furthermore, in the median case scenario, the proposed TF-MFA model is 0.84%, 1.82%, 8.49%, 26.97%, 9.61%, and 7.42% better than existing ABC + BA, ACO, CSA, KH, WOGA, and ILWOA methods for 40 counts of VM. Thus, it is evident from the table that the presented work is much more sufficient for load balancing during VM migration. Additionally, for 40 counts of VM, the offered TF-MFA approach outperforms the current ABC + BA, ACO, CSA, KH, WOGA, and ILWOA approaches by 0.84%, 1.82%, 8.49%, 26.97%, 9.61%, and 7.42% in the median case circumstance. Overall, Table 6 makes it clear that the amount of work that has been given is far more adequate for managing load throughout virtual machine relocation.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline
**No. of VM** & **TF-MFA** & **ILWOA** & **WOGA** & **KH** & **CSA** & **ACO** & **ABC + BA** \\ \hline
10 & 88.80 & 287.15 & 234.93 & 291.75 & 252.03 & 259.1 & 295.65 \\
20 & 190.82 & 268.47 & 256.54 & 268.06 & 254 & 247.12 & 272.99 \\
30 & 221.24 & 237.11 & 69.62 & 248.36 & 243.15 & 247.69 & 227.09 \\
40 & 153.47 & 281.76 & 244.26 & 292.56 & 294.68 & 276.49 & 231.2 \\
50 & 147.57 & 281.47 & 74.481 & 257.07 & 301.89 & 208.99 & 217.64 \\ \hline \end{tabular}
\end{table}
Table 5: Placement costs for different VMs to a PM for the current and potential models.
# GBCNet: In-Field Grape Berries Counting for Yield Estimation by Dilated CNNs
## Materials and Methods
### Performance Metrics  
To evaluate the GBCNet model performance we adopt the most common metrics employed in both agricultural yield estimation and crowd-counting domains [13; 17; 44; 45], i.e., Mean Absolute Error (MAE) and Mean Squared Error (MSE). These are defined as follows:  
\[MAE=\frac{1}{N}\sum_{i=1}^{N}\left|C_{i}-C_{i}^{GT}\right| \tag{5}\]  
\[MSE=\sqrt{\frac{1}{N}\sum_{i=1}^{N}(C_{i}-C_{i}^{GT})^{2}}\, \tag{6}\]  
where \(C_{i}\) is the estimated count and \(C_{i}^{GT}\) is the ground truth count associated to image \(i\). The estimated count is equal to the integral of the output density map. These two metrics represent a measure of accuracy (MAE) and robustness (MSE) of the model.  
To estimate crop yield it is important to consider the performances obtained when considering the cumulative sum of the outputs and ground truths as well. To this end, we also employ Overall MAE, defined as  
\[Overall\ MAE=\left|\sum_{i=1}^{N}C_{i}-\sum_{i=1}^{N}C_{i}^{GT}\,\right| \tag{7}\]  
providing information on the performances that can be obtained in practical applications of the system.
# Functional Traits of Plant Species Suitable for Revegetation of Landfill Waste from Nickel Smelter
## 1 Introduction
### Characteristics of Investigated Plant Species  
To determine the functional traits, three plant species were selected, which represent different life forms--therophyte, hemicryptophyte and phanerophyte (in the juvenile stage).  
Figure 1: Map of Slovakia with the landfill waste location.  
_Populus alba_ (Figure 3, white poplar--phanerophyte [17] in the juvenile stage) is a fast-growing tree with deciduous leaves, which can grow to a height of 20 to 35 m. Dark green, lobed leaves, the underside is slightly felted, coloured white. It is widespread in Central, Southern and Eastern Europe, across Siberia to the Yenisei River, in Asia to the western Himalayas. It was introduced to Micronesia, Australia and New Zealand and is also spreading invasively in the Canary Islands. It is a tree of floodplain forests; it occurs in coastal shrubs and on river alluvium. In the soft meadow, it forms communities with _Salix alba_ (white willow) and _Populus nigra_ (black poplar). White poplar is a light-loving tree, only younger individuals tolerate weaker shading. It tolerates changes in water regime very well and is also resistant to long-term floods. It is secondary to quarries in sandpits, brickyards, embankments, landfills, mining heaps and similar places with exposed surfaces [18]. In Slovakia, it is widespread in the lowlands [19].  
_Calamagrostis epigejos_ (Figure 4, bush grass--hemicryptophyte [17] is a perennial grey-green bushy grass that has strong creeping subsoils with long shoots. It inhabits mainly sparse forests, log cabins, quarries, neglected pastures and ruderal areas. In pastures and meadows, this species is invasive and suppresses various meadow communities. Bush grass is an ecologically plastic and can colonize multiple substrates. It is widespread in Europe, North Asia, and secondarily in North America and South Africa. It inhabits the whole territory of Slovakia [20].  
Figure 3: _Populus alba_ growing in the landfill.  
Figure 2: Actual view of the landfill.  
_Diplotaxis muralis_ (Figure 5, annual wall-rocket--therophyte [17]) is an annual, biennial, rarely perennial herb, 15-50 cm tall. It inhabits mainly rubbles, fields, fallows and vineyards; it also occurs along roads, on landfills, ruderalized lawns, composts and various anthropogenic habitats. It occurs mostly on clayey or gravelly, nutrient-rich soils [21]. It is originally widespread throughout Central and Southern Europe (up to Northwest Africa), secondarily it occurs in Northern Europe, in parts of Russia and northern China, in South Africa, as well as in Australia and Tasmania, New Zealand, New Mexico, in almost the entire United States and southern Canada, and Central and South America [22]. In Slovakia, it is a common species, especially in warmer areas and in habitats with mild to medium soil moisture.
# Lightweight Transformer with Adaptive Rotational Convolutions for Aerial Object Detection
## 3 Proposed Methodology
### Adaptive Rotational Convolution Module
#### 3.2.2 Dynamic Kernel Rotation  
The \(\text{F}\in\mathbb{R}^{H\times W\times C}\) input feature map to a convolutional layer, where \(H,\ W\), and \(C\) represent the height, width, and number of channels, respectively. A standard convolution operation with a kernel \(\text{K}\in\mathbb{R}^{k\times k\times C}\) where \(k\) is the kernel size, is defined as:  
\[\text{F}_{out}(i,j)=\sum_{m=1}^{k}\sum_{n=1}^{k}\text{K}(m,n)\cdot\text{F}(i+ m,j+n), \tag{8}\]where \(\mathrm{F}_{out}\in\mathbb{R}^{H^{\prime}\times W^{\prime}\times C^{\prime}}\) is the output feature map after the convolution.  
In the ARC module, instead of using a fixed kernel \(\mathrm{K}\), we introduce a dynamic rotation of the kernel based on the local orientation \(\theta(i,j)\) detected within the receptive field centered at \((i,j)\). The rotated kernel \(\mathrm{K}_{\theta}\) is given by:  
\[\mathrm{K}_{\theta}(m,n)=\mathrm{R}_{\theta}\mathrm{K}(m,n), \tag{9}\]  
where \(\mathrm{R}_{\theta}\) is a rotation matrix that rotates the kernel by the angle \(\theta\). The rotation matrix \(\mathrm{R}_{\theta}\) is defined as:  
\[\mathrm{R}_{\theta}=\begin{bmatrix}\cos\theta&-\sin\theta\\ \sin\theta&\cos\theta\end{bmatrix}, \tag{10}\]  
Applying this rotated kernel in the convolution operation results in the following output:  
\[\mathrm{F}_{out}(i,j,\theta)=\sum_{m=1}^{k}\sum_{m=1}^{k}\mathrm{K}_{\theta} (m,n)\cdot\mathrm{F}(i+m,j+n), \tag{11}\]  
This dynamic adjustment allows the convolutional filter to align with the dominant orientation of features in each local region, improving the model ability to detect and represent objects that are rotated in the image.
## 8 Performance Comparison among TMD, TMDI, and Pendulum PTMD
### Performance Comparison
The peak inter-story drift ratio under the El-Centro earthquake is 1.72%. However, the limiting maximum drift ratio for the long-period structure is 2% [93]. Conversely, the 42-story timber building studied in this paper may be safe under moderate seismic excitations, keeping the main force-resisting system in mind. However, nonstructural elements such as ceilings, piping, and partitions may experience considerable damage, resulting in substantial economic loss. Thus, it is crucial to reduce excessive vibration by introducing damping solutions. The pendulum PTMD lowered the peak inter-story drift ratio by 40%. Figure 22 shows the peak inter-story drift ratio and STD drift ratio along with the building's height. Figure 23a shows the STD base moment, and Figure 23b shows the maximum base moment. The pendulum PTMD reduced the STD base moment by 41% and the maximum base moment by 14.5%. The performance of both TMD and TMDI to decrease the base moment is poor compared to the pendulum PTMD, as shown in Figure 23.  
with the stiffness of the building. In this paper, the control performance of TMD, TMDI, and pendulum PTMD is examined by changing the primary stiffness of the whole structure by \(\pm 15\%\). The comparative performance of all three devices under the El-Centro earthquake is presented in Figure 16.0 Responses of the uncontrolled building under seismic excitation show that by increasing the structure's stiffness, the displacement response is reduced; however, the acceleration response is increased. Figure 16.1 Base shear at each floor: (**a**) STD shear, (**b**) maximum shear. Figure 16.2 The floorwise shear force under El-Centro earthquake ground acceleration: (**a**) STD shear, (**b**) maximum shear. Figure 16.3 The base moment at each floor: (**a**) STD moment, (**b**) maximum moment. Figure 16.4 Interstory drift ratio under earthquake: (**a**) peak drift-ratio, (**b**) STD drift-ratio. Similarly, decreasing the building stiffness increases the displacement response while peak acceleration is reduced. It is observed that the proposed pendulum PTMD has much better potential to reduce responses of the building considering the stiffness uncertainty. Displacement, acceleration, base shear, base moment, and drift ratio are reduced more by pendulum PTMD than TMD and TMDI when the stiffness of the building is changed by \(\pm 15\%\). The control performance of the pendulum PTMD remains almost the same. However, the performance of TMD and TMDI is reduced significantly due to detuning. This observation demonstrates the robustness of pendulum PTMD. It can address the challenges of physical civil infrastructure to be resilient and sustainable over its service lifetime, under service and long-term conditions, including increased demands due to climate change adaptation and other emerging stressors (including single and multiple hazard events).
## 4 Results
### Performance Curves  
Based on the experimental data near 31,000 RPM given in Figure 9, the pressure ratio and isentropic efficiency performance curve of the sCO\({}_{2}\) single-stage centrifugal compressor are plotted in Figures 10 and 11. The single-stage centrifugal compressor achieves a maximum pressure ratio close to 2.0, and the mass flow of the compressor is maintained at about 13.5 kg/s, which is close to the surge boundary of the compressor. The maximum experimental flow rate is about 18 kg/s, at which the pressure ratio is 1.75, close to the choke boundary.  
Figure 9: Original experimental data and historical curves.  
Figure 11 shows the isotropic efficiency performance curve of 31,000 \(\pm\) 1000 RPM. It can be seen that the isentropic efficiency of the compressor near the surge boundary is as low as 0.65, the isentropic efficiency near the choke boundary is 0.70, and the compressor reaches the maximum isotropic efficiency of 0.8 at about 16-17 kg/s. It should be noted that inlet and outlet parameters, particularly temperature, have significant influence on the calculation accuracy of isentropic efficiency. According to Equation (2) and [PERSON]'s uncertainty calculation method [13], Figure 11 shows the isentropic efficiency's uncertainty at 31,000 \(\pm\) 1000 RPM. As can be seen in Figure 11, the uncertainty of isentropic efficiency is approximately \(\pm\)0.1, which may be unsuitable for quantitative analysis.  
in this section for experimental data approaching 31,000 RPM. Despite the fact that there is no recognizable phase change process at 31,000 RPM, the supercritical state fluctuates according to its physical properties. It can be further classified as supercritical-liquid-like (Sc-liquid-like) phase and supercritical-gas-like (Sc-gas-like) phase according to the pseudo-critical line.  
In Figure 12, the yellow dot represents the design point, the green line the pseudo-critical line, and the pink line the critical line. The design point can be seen to be on the pseudo-critical line. The inlet condition of this experiment, on the other hand, deviates slightly from the design point; all fall in the supercritical-liquid-like region, meaning that if condensation takes place, the liquid phase will have a high mass fraction and the gas phase will occupy the space of the liquid phase. The detailed condensation phenomenon will be analyzed in Section 5.4.
## 3 Results and Discussion
### Application of Artificial Intelligence, Predictive Analytics, and Digital Twins
#### 3.4.4 Explainable AI and Data Sharing Frameworks for Collaborative Learning  
As AI models become more advanced and integrated into decision-making processes, the need to improve their transparency and interpretability has become increasingly relevant [69; 70; 73]. Explainable AI (XAI) techniques address this by clarifying how AI-based decisions are made, particularly in optimization and control tasks within microgrids [74; 75; 76].  
Collaborative learning approaches, such as federated learning, offer alternatives for model development while protecting sensitive data [49; 77; 78]. These methods allow multiple microgrids or energy management systems to train models collectively without exchanging raw data.  
Future efforts should focus on combining XAI techniques with collaborative learning frameworks to achieve transparent, secure, and efficient AI deployment in microgrids. Establishing common standards for data sharing and model evaluation will support cooperation between research institutions and industry organizations [34; 42; 48].
## 4 Results and Discussion  
### Test of the Ag/AgCl Pseudo-Membrane  
#### 4.1.1 Preparation of the Ag/AgCl Electrodes  
Silver plates (14.5 \(\times\) 14.5 cm\({}^{2}\)) were cleaned with concentrated nitric acid. Anodizing of each plate was performed in a bath containing of 480 g of NaCl in 8 L of demi water. The silver plate was clamped between two titanium mesh electrodes covered with an Ru-Ir metal-oxide mixture. A distance of about 1 cm between the silver plate and the titanium electrode was fixed by four small rubber space holders at the corners. An electrical current of 2.496 A (59 A/m\({}^{2}\)) was applied for 30 min. Figure 3 shows the U-t curves. With a mean value of \(a\) (\(a\) = 0.086 V/min = 0.00143 V/s) from Table 6, we calculated with Equation (9) Faradic area resistance, \(R_{F}\) = 0.039 \(\Omega\) C\({}^{-1}\) m\({}^{4}\).  
After 1 min, using Equation (7), the area resistance of one AgCl layer on the silver plate was \(14.5\,\Omega\) cm\({}^{2}\). Because this layer was present on both sides, the total area resistance of the pseudo-membrane was 29.0 \(\Omega\) cm\({}^{2}\) after 1 min, larger than normal AEMs by at least a factor of 10. In our case, the applied anodizing current was 59.4 A/m\({}^{2}\), and assuming the same current density in the RED and ED application, the feed water switch in that stack should be performed within 1 min. During operation, the resistance of the pseudo-membrane will be more or less constant: the decrease on one side is compensated by growth on the other side.  
An acceptable switch period (the time between two feed water switches) is dependent on the residence time of the feed waters. In our experiments, we used stacks with a flow channel length of 10 cm and feed water velocity of about 1 cm/s, resulting in a residence time of 10 s. Thus, during the switch, the stack was at least 10 s ineffective. With a half-switch time of 1 min, this 10 s meant a considerably loss. Otherwise, a longer switch period requires a thicker AgCl layer, with correspondingly higher ohmic losses. A solution may use lower current densities during ED/RED (e.g., 10 A/m\({}^{2}\)), resulting in relatively shorter dead time.
## 2 Automatic Crop Type Mapping Using Spectral Indices
The authors chose seven spectral indicators such as NDVI\({}_{04}\) (Normalized Difference Vegetation Index), NDWI\({}_{04}\) (Normalized Difference Water IndexNDBI\({}_{04}\) (Normalized Difference Building Index), etc., and designed a new indicator to better classify the crop patterns by investigating the sampling points. For each study region, they labeled the training sample, calculated the value of spectral indicators in a specific phenological period, and compared the differences in spectral characteristics that occurred in different crop species. Thus, it was possible to estimate crop types with an average \(OA\) of 94% for an area of two million km\({}^{2}\). Moreover, [PERSON] et al., (2019) [18] applied various machine learning and deep learning methods for crop type classification based only on the Landsat Vegetation Index. XGBoost provided an accuracy of 85.54% and an F1 score of 0.73, whereas a one-dimensional convolutional neural network (Conv1D) yielded an \(OA\) of 85.54% and F1 score of 0.73.
# Stability Prediction of Soil Slopes Based on Digital Twinning and Deep Learning
## 3 Methods
### K-Fold Cross Validation  
To evaluate the generalization ability of the model, this paper adopts the K-fold cross-validation method. The commonly used K value ranges from 5 to 12, and relevant studies have confirmed that 10 is widely used [30]. This paper adopts 10-fold cross-validation, and the principle is shown in Figure 16.  
K-fold cross-validation steps:  
(1) Step 1: The original data were randomly divided into K parts by non-repeated sampling.  
Figure 16: K-fold cross-validation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer** & **1D CNN** & **2D CNN** \\ \hline
**Input Layer** & \(\mathbf{410\times 1}\) & \(\mathbf{10\times 41}\) \\ \hline \multirow{4}{*}{Conv\_1} & Kernel size & [2 1] & [2 2] \\  & Kernel num. & 32 & 32 \\  & Stride & [1 1] & [1 2] \\  & Pad & 0 & 0 \\  & Activation & Leaky ReLU & Leaky ReLU \\ \hline \multirow{4}{*}{Conv\_2} & Kernel size & [2 1] & [2 2] \\  & Kernel num. & 64 & 64 \\  & Stride & [2 1] & [2 2] \\  & Pad & None & None \\  & Activation & Leaky ReLU & Leaky ReLU \\ \hline \multirow{4}{*}{Conv\_3} & Kernel size & [2 1] & [2 2] \\  & Kernel num. & 128 & 128 \\ \cline{1-1}  & Stride & [1 1] & [1 1] \\ \cline{1-1}  & Pad & None & None \\ \cline{1-1}  & Activation & Leaky ReLU & Leaky ReLU \\ \hline \multicolumn{2}{c}{Fully Connected Layer} & 1 & 1 \\ \hline \multicolumn{2}{c}{Output Layer} & Regression Layer & Regression Layer \\ \hline \hline \end{tabular}
\end{table}
Table 5: Parameters of 1D CNN and 2D CNN.  
2. Step 2: (K \(-\) 1) parts are used for model training, and the remaining 1 part is used for model testing.
3. Step 3: Repeat Step 2 K times to obtain the evaluation results of the K models.
4. Step 4: Calculate the average value of the K-fold cross-validation results as the performance evaluation of the model.
## 4 Consensus Algorithm for Optimal Obstacle Avoidance
### Consensus of Obstacles in AUV Trajectories  
In this part, we consider different places and times of the appearance of obstacles. Assume that the first obstacle appears in the trajectory of AUV 2 (23, 24, 22), the radius of the obstacle is \(\mathbf{r}_{1}=0.7\), and the diagnostic zone is \(\mathbf{R}_{1}=1.5\). The second obstacle at (14, 17, 20) does not exist in the trajectory of any AUV. The final obstacle is assumed to appear in the trajectory of AUV 1 at (\(7,7,7\)). The radius and diagnostic area of the obstacle are \(\mathbf{r}_{1}=1\) and \(\mathbf{R}_{1}=2.5\), respectively. Figure 5 depict the simulation results.  
It can be observed from Figure 5 that all AUVs could avoid multiple obstacles on the trajectory and finally reached a consensus point (15.28, 18.59, 15.69), which differed from the previous consensus point (15.59, 18.8, 15.59). Figure 6, which shows the control input of the AUVs and time history of the position, respectively, reveals that the proposed optimal control law could achieve consensus as well as multi-obstacle avoidance.  
Figure 4: Time histories of positions and control inputs of five AUVs with no obstacles in trajectories.  
Figure 3: (a) Consensus trajectories of five AUVs with no obstacles. (b) Consensus trajectories of five AUVs with no obstacles in X–Y plane.