esa-sceva/satcom-chunk-collection huggingface dataset:
# NUDIF: A Non-Uniform Deployment Framework for Distributed Inference in Heterogeneous Edge Clusters
## 2 Related Work  
In the context of distributed inference deployment, optimization methods have become a core component in enhancing performance and resource utilization. These methods can be categorized into two primary categories: joint optimization of model partitioning and deployment, and staged deployment optimization methods.  
The first category emphasizes the joint optimization of model partitioning and resource allocation to enhance overall system performance. For example, CoopAI [9] uses dynamic programming to refine model partitioning while dynamically adjusting the computation-communication balance. SCADS [10] applies piecewise convex optimization for task scheduling, aiming to minimize system latency. EosDNN [11] employs intelligent algorithms to optimize cross-platform migration strategies for deep neural networks. JointDNN [2] optimizes distributed computing in mobile cloud environments using integer linear programming, focusing on minimizing energy consumption and maximizing computational efficiency.  
Conversely, the second category targets deployment optimization following model partitioning to improve system adaptability. DINA [12] facilitates adaptive task offloading within multi-layer fog computing architectures using matching theory, aiming to balance load and reduce latency in heterogeneous environments. TREND-WANT [13] optimizes hierarchical deployment in 5G networks using dynamic programming techniques to balance the trade-off between computation and communication. DistInference [14] and OULD [15] focus on optimizing CNN layer allocation for unmanned aerial vehicle (UAV) clusters, minimizing end-to-end latency while considering mobility and communication constraints. In the domain of model compression, EdgeAI [16] achieves distributed parallel inference through class-aware pruning, while DeepThings [17] enhances throughput using block fusion and dynamic load balancing. EdgeShard [6] employs dynamic programming to optimize task allocation for large language model (LLM) inference, focusing on reducing inference time and enhancing scalability in edge environments.  
The existing methods primarily exhibit a limitation in optimizing deployment for single-instance models, which fails to fully utilize resources and often leads to bottlenecks due to imbalances between processing stages during batch processing. In contrast, our NUDIF framework leverages non-uniform deployment and optimized constraints to achieve enhanced resource utilization and balanced capabilities across stages. By modeling the deployment strategy as a mixed-integer nonlinear programming (MINLP) problem, we are able to optimize both resource allocation and throughput, overcoming the limitations of traditional approaches and enabling scalable, flexible deployment strategies.
# Responses of Medicinal and Aromatic Plants to Engineered Nanoparticles +
## 4 Effect of Metal-Based NPs on Medicinal and Aromatic Plants
### Silver-Based NPs
#### 4.1.5 Cytotoxic and Genotoxic Effects of AgNPs  
AgNPs with particle sizes >100 nm were found to penetrate the root tip cells of _Al-lium cepa_ and adversely affected stages of cell division resulting in formation chromatin bridge, stickiness, disturbed metaphase, multiple chromosomal breaks, and cell disintegration, whereby at application of 100 ppm AgNPs mitotic index (MI) decreased from 60.30% (control) to 27.62% [196]. Mitigation of genotoxic and cytotoxic effects of AgNPs in onion root tips reflected by a decrease of MI, elevated chromosome aberrations, and tail DNA caused by oxidative stress can be attenuated by using antioxidants (e.g., vitamin E), pretreatment being more effective than combined treatment with AgNPsPs [197]. Cytotoxic and genotoxic impact of Ag nanorods on _A. cepa_ roots depended on their concentration, a dose of 15 \(\upmu\)M being more toxic than doses of 5 and 10 \(\upmu\)M, respectively [198]. Although polyvinylpyrrolidone (PVP)-coated as well as uncoated AgNPsPs were found to be genotoxic, cytotoxic, and induce oxidative stress in _A. cepa_ roots, even at low concentrations; under exposure to visible-light, their aggregation state will be altered, resulting in reduced toxicity [199]. Due to application of 25 and 50 mg/L AgNPs to _V. planifolia_, only a small MI decrease was observed and genotoxic effects were minimal, with < 5% of the total chromatic aberrations and 3 micronucle in 3000 cells, despite the long-time exposure to AgNPs [200].  
In general, considering the beneficial impact of AgNPs on enhanced production of secondary metabolites, they could be recommended to be used, particularly for production of stevioside in callus culture of _Stevia rebaudiana_ L. [126] and for enhanced production of rosmarinic acid by foliar spraying of _Salvia officinalis_ L. plants [128] (Table 1).
## 3 Results and Discussion
### TVEE Regulates MMP-2 via MAPK Signaling Pathways in HDFs  
Excessive ROS production by H\({}_{2}\)O\({}_{2}\) in HDFs can activate MAPK signaling pathways [15]. To determine the signal pathway of the protective effect of TVEE, we evaluated whether the protective effect of TVEE in HDFs exposed to H\({}_{2}\)O\({}_{2}\) occurs via the regulation of the MAPK pathway. The phosphorylation of MAPK (ERK, JNK, and p38) increased compared with that of the control group when treated with 0.85 mm H\({}_{2}\)O\({}_{2}\) (Figure 4). However, phosphorylation was significantly reduced after TVEE treatment at concentrations of 50, 100, and 200 \(\upmu\)g/mL. In particular, ERK and JNK were the most effective in inhibiting phosphorylation in a concentration-dependent manner. Therefore, TVEE exhibited the protective effects against H\({}_{2}\)O\({}_{2}\)-induced cell injury in HDFs via the regulation of the MAPK pathways. The effect of TVEE on MMP-2 protein expression was confirmed (Figure 4). When treated with TVEE at concentrations of 50, 100, and 200 \(\upmu\)g/mL, the protein expression level of MMP-2 was significantly decreased in a concentration-dependent manner. Previous studies have shown that MMP production was inhibited via MAPK inhibition [30]. These results confirm that TVEE reduces MMP-2 expression by affecting the regulation of the MAPK signaling pathway in HDFs.  
Figure 3: Intracellular ROS production in H\({}_{2}\)O\({}_{2}\)-induced HDFs. (**a**) ROS fluorescence intensity was detected by DCFH-DA using a fluorescence microscopy. Values are expressed as mean \(\pm\) SD. **\(p\)** < 0.01, ***\(p\)** < 0.001 indicate values compared with only the H\({}_{2}\)O\({}_{2}\)-induced group. (**b**) Image of the intracellular ROS production by fluorescence microscopy (scale bar = 500 \(\upmu\)m).
# Failure Mode and Effects Analysis Considering Consensus and Preferences Interdependence
## 3 IVPFGBM and IVPFWGBM Operators
### Failure Modes Ranking  
As a particularly pragmatic and reliable tool, the MABAC approach is applied to solve MCDM problems because it can obtain a stable solution. However, this method fails to capture the interdependence between failure modes. Therefore, an improved MABAC method is utilized to determine the ranking order of failure modes in which the border approximation area is calculated by the IVPFGBM operator instead of the geometry mean operator.  
**Step 5:** Compute the weighted group decision matrix \(Y\).  
The weighted decision matrix \(Y=\left(y_{ij}\right)_{m\times n}\) can be determined by the following formula:  
\[y_{ij}=w_{j}d_{ij}=\left(\left[\sqrt{1-\left(1-\underline{\mu}_{p_{j}}^{2} \right)^{w_{j}}},\sqrt{1-\left(1-\overline{\mu}_{p_{j}}^{2}\right)^{w_{j}}} \right],\left[\left(\underline{\
u}_{p_{j}}\right)^{w_{j}},\left(\overline{ \
u}_{p_{j}}\right)^{w_{j}}\right]\right), \tag{25}\]  
where \(y_{ij}\) is a weighted IVPFN and \(d_{ij}\) are the elements of the collective evaluation matrix \(D\).  
**Step 6:** Determine the border approximation area vector \(G\).  
The border approximation area for each risk factor is computed by applying the IVPFGBM operator as:  
\[g_{j}=IVPFGBM(y_{1j},y_{2j},\cdot\cdot\cdot,y_{mj})=([\underline{\mu}_{p_{j}},\overline{\mu}_{p_{j}}],[\underline{\
u}_{p_{j}},\overline{\
u}_{p_{j}}]). \tag{26}\]  
Then, the border approximation area vector \(G\) can be constructed in the following format \(G=\left(g_{1},g_{2},\cdot\cdot\cdot,g_{n}\right)\).  
**Step 7:** Construct the distance matrix \(X\).  
The distance of the failure modes from the border approximation area (See Figure 2) is calculated by Equation (2) to construct the distance matrix \(X=\left(x_{ij}\right)_{m\times n^{\prime}}\), where  
\[x_{ij}=\left\{\begin{array}{ll}d(y_{ij},g_{j})&if\ s(y_{ij})\geq s(g_{j})\\ -d(y_{ij},g_{j})&if\ s(y_{ij})<s(g_{j})\end{array}\right.. \tag{27}\]  
**Step 8:** Determine the ranking of failure modes.  
All failure modes will be included in approximation areas that consist of an upper approximation area \(G^{+}\), a border approximation area \(G\), and a lower approximation area \(G^{-}\). The ideal failure mode (\(FM^{+}\)) is located in the upper approximation area, while the anti-ideal failure mode (\(FM^{-}\)) is located in the lower approximation area (See Figure 2). Consequently, in order to select the ideal failure mode, it is necessary to have as many risk factors as possible belonging to the upper approximation area.  
We can obtain the closeness coefficient \(\theta_{i}\) for each failure mode by computing the sum of the row elements of the distance matrix \(X\).  
\[\theta_{i}=\sum_{j=1}^{m}x_{ij},i=1,2,\cdot\cdot\cdot,m. \tag{28}\]  
The risk priority of failure modes is obtained according to the decreasing order of the closeness coefficient.
# Complementary Segmentation of Primary Video Objects with Reversible Flows
## 5 Experiments
### Detailed Performance Analysis
#### 5.3.2 Effectiveness of Neighborhood Reversible Flow
However, the superpixel-wise predictions are relatively coarse and may affect the following process. To explore this effect, we converted the foreground/background maps and their fusion maps in cases R-Init. FGp/BKp from pixel-wise to superpixel-wise, as shown in Table 4. Fortunately, both \(F_{\beta}\) and mIoU scores of foreground (background) maps only slightly decrease by 0.003 (0.004), and the mT scores increase by 0.009, while the negative effect on fusion maps mainly manifests in mT scores, which can be improved by the propagation of neighborhood reversible flow. Thus, the trade-off is worthy. This also hints at the important effect of neighborhood reversible flow on temporal stability or consistency. **Parameter setting**. In the experiment based on CCNN, we smoothly varied two key parameters used in NRF, including the \(k_{0}\) in constructing neighborhood flow and the \(\lambda_{c}\) that controls the strength of temporal propagation. As shown in Figure 7, a larger \(k_{0}\) tends to bring slightly better performance, while our approach performs the best when \(\lambda_{c}=0.5\). In these experiments, we set \(k_{0}=15\) and \(\lambda_{c}=0.5\) when constructing the neighborhood reversible flow. **Selection of color spaces**. In constructing the flow, we represented each superpixel with three color spaces. As shown in Table 5, a single color space performs slightly worse than their combinations. Actually, using multiple color spaces has been proved to be useful in detecting salient objects [2], as multiple color spaces make it possible to assess temporal correspondences from several perspectives with a small increase in time cost. Therefore, we choose to use RGB, Lab and HSV color spaces in characterizing a superpixel. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Step** & **mAP** & **mAR** & \(F_{\beta}\) & **mIoU** & \(T\) \\ \hline R-Init. FGp (Sup.) & 0.765 & 0.924 & 0.797 & 0.723 & 0.133 \\ R-Init BKp (Sup.) & 0.759 & 0.926 & 0.792 & 0.719 & 0.133 \\ R-Init FGp + BKp (Sup.) & 0.814 &.881 & 0.829 & 0.738 & 0.129 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance of superpixel-wise initialization by CSNet on **VOS**. FGp: foreground branch, BKp: background branch. Sup. is short for superpixel. Figure 6: Performance of the proposed neighborhood reversible flow. The first row is the video sequences from **Youtube-Objects**[1], the second row is the corresponding initialized foreground maps, and the third row is the optimized results by neighborhood reversible flow.
# Factorial Design Analysis for Localization Algorithms
## 4 Results
### Level Noise  
In the evaluation scenario proposed in [19] the noise level was considered from 4 to 12 dB; however, in this experiment 2 and 10 dB critical levels are considered. The noise level of 2 dB represents a value that does not impact the RSS, and therefore does not affect the localization algorithms' performance either, while a noise level of 10 dB does have a considerable impact on the performance metrics of the localization algorithms evaluated in this scenario.
## 3 Results and Discussion
### Feature Selection for the First Layer  
We first picked out the most contributing characteristics with a step of 2 for the other three types of characteristics, except for SNC with a step of 1. We decided which characteristics were retained based on the average results of 5-fold cross-validation test running 5 times for both the first and the second layers. To avoid wordiness, we only described the process of feature selection for the first layer. For the characteristic of SNC, the best prediction performance was achieved at all the four features (Table S2, Supplementary Materials). For the DNC, we sorted the 16 values according to the F-scores, and then selected 2, 4,..., 14, 16 features step by step. As listed in Table S3, the best performance achieved at 8 di-nucleotides selected (i.e., CG, UG, GA, AG, UA, CC, UU, and AU), with an _Sn_ of 86.55%, an _Sp_ of 82.05%, an _Acc_ of 84.29%, and an _MCC_ of 0.6870. The same processes were made for TNC and physicochemical properties (See Tables S4 and S5 (Supplementary Materials) for detail description) and the best performance parameters for these features are listed in Table 2. As we can see from Table 2, the DNC features achieved the best performance with an _Acc_ of 84.29% and an _MCC_ of 0.6870; on the contrary, the SNC feature achieved the worst prediction results with an _Acc_ of 62.96% and an _MCC_ of 0.2791. At last, all the selected characteristic vectors were combined together to get the higher results with an _Acc_ of 88.95% and an _MCC_ of 0.7791. Both the _Sn_ and _Sp_ were satisfactory.  
However, the \(Sp\) was 77.3%, less than 77.60% achieved by using TNC only. From the analysis, the prediction results were not as satisfactory as those for the first layer. Therefore, we had to add another type of features to further improve the prediction performance. Inspired by the \(k\) nearest neighbor (KNN) classification algorithm, we used KNN to embody the distribution of neighbor sequences [32]. Unlike in the samples investigated above, the lengths of samples in \(S_{inst}^{+}\) and \(S_{non-inst}^{+}\) were not the same. To deal with this problem, the similar strategy was adopted to consider 10 nucleotides in N-terminal and C-terminal separately. The algorithm of using KNN to extract features is described in detail in [33]. The KNN features (\(k\) = 10) of N-terminal were firstly added to the original combination of BPB(15) + SNC(4) + DNC(10) + TNC(48) + PP(24), which increased the \(Sp\) to 79.3%, and \(Sn\) to 80.1%. Then, the KNN features (\(k\) = 10) of C-terminal were further added, which increased the \(Sp\) to 83.6%, and \(Sn\) to 84.3%.
## 6 Improvement of the Steering Arm  
### Improvement Analysis  
The steering arm is generally made of castings or forgings, with the casting round corners, bosses, pits, cone holes, bolt holes, counter bores, and so on, and its shape is irregular, as shown Figure 7. According to the stress distribution on the steering arm, its failure is from the combined effect by the bending and twisting. Based on the premise of no motion interference between the steering arm with other parts, three improvement schemes can be used to enhance its strength, as shown in Figures 17 and 18. These schemes have respective advantages and disadvantages, and their descriptions and discussions are as follows.  
Figure 17: Improvement scheme 1.  
**(1) Improvement scheme 1.** The scheme is to alleviate the torsional effect around the X axis by adjusting the position of the spherical joint of the ball pin on the steering arm, as shown in Figure 17. No matter which role--withstanding the tire force or performing the steering--reducing the torsion is beneficial. However, the scheme will result in the modification of the ball pin and even the base of the steering arm.  
**(2) Improvement scheme 2.** The scheme increasing the thickness is to augment the moment of inertia of the cross-sectional area about the Z axis, as well as the polar moment of inertia about the X axis, as shown in Figure 18. However, the enhanced strength based on the scheme is not as evident as augmenting the width (i.e., improvement scheme 3), according to the qualitative analysis from mechanics of materials [27]. In addition, the scheme will similarly result in the modification of the ball pin, or even a change in position of the spherical joint, and the change may have an adverse effect on the kinematic characteristics.  
**(3) Improvement scheme 3.** The scheme, whose purpose is the same as the improvement scheme 2, is to increase the width, as shown in Figure 18. However, the kinematic analysis should be conducted to avoid motion interference between the improved steering arm and other parts.  
Having a big modification in the steering system, schemes 1 and 2 may be not very applicable in this stage, for the steering arm needs to be replaced fast, but they can be adopted for the development of new vehicle in the near future. To sum up, improvement scheme 3 will be used to enhance the strength of the steering arm in this paper.
# Sentinel-2 Reveals Record-Breaking Po River Shrinking Due to Severe Drought in 2022
## 1 Introduction
In fact, during a drought, the variation in flow rate shows a reduction in riverbed width, which results in a gradually decreasing PWC in riverbank pixels due to changes in the proportions of subpixel water and non-water components. Another advantageous feature is the possibility of using virtual constellations of satellites to increase the temporal revisit and consequently increase the monitoring capacity [33]. In recent years, the availability of a wide number of small satellites mounting low-cost optical multispectral sensors, even if equipped with a spectral configuration, not including some radiometric intervals, opened the way to the characterization of slightly wide or highly branched riverbeds. For these reasons, the objective of this study is to present a simple but effective and portable approach for the monitoring of river hydromorphology through remote sensing time series analysis. Simple, because it makes use of a fuzzy transform function on a single spectral index; effective, because it takes into account the gradient of water cover, expressed as the percentage of water content in the pixel, and exploits dense time series; and portable, because the selected spectral index is calculated on the spectral bands available in most multispectral optical sensors. It is based on the extraction of PWC from satellite optical data and the use of relative metrics, which can be applied to a variety of high and very-high-resolution optical multispectral sensors and different geographic sites. The effectiveness of the methodology is demonstrated in the case study of the Po River shrinking during the record-breaking drought of year 2022, through the use of open-access Copernicus Sentinel-2 satellite data.
# Energy Efficient Clustering Protocol for FANETS Using Moth Flame Optimization
## 4 Experimental Results and Analysis
### Cluster Lifetime  
The cluster lifetime is when the cluster exists, from its formation to its dissolution. The UAV with the best fitness value is selected as the cluster leader when the clustering method is completed. When different operations are performed on the UAV, the fitness value of the UAV steadily declines over time. The CH election process is triggered again when the value falls below a certain threshold. A shorter cluster lifetime indicates that the clustering method must be conducted more frequently, increasing the amount of communication and computing that must take place in the network. As seen in Figures 16-18, our EECP-MFO algorithm outperforms the GWO, PSO, and ACO algorithms in terms of performance. As a result of these findings, it can be concluded that increasing the number of UAVs in the network reduces the longevity of the cluster. As the number of SNs in the network grows, the network's topology changes more often, making the grid less reliable.  
Figure 14: Energy consumption vs. number of nodes (\(3000\times 3000\) m\({}^{2}\)).  
Figure 13: Energy consumption vs. number of nodes (\(2000\times 2000\) m\({}^{2}\)).  
Figure 16: Cluttering lifetime vs. number of nodes (1000 \(\times\) 1000 m\({}^{2}\)).  
Figure 17: Cluttering lifetime vs. number of nodes (2000 \(\times\) 2000 m\({}^{2}\)).  
Figure 15: Probability of success for packet.
# Focal Dice Loss-Based V-Net for Liver Segments Classification
## 2 Materials and Methods
### Convolutional Neural Network  
Encoder-decoder architectures (especially those that are U-shaped) have drawn much attention in the biomedical semantic segmentation domain [28]. Important examples are the U-Net model for 2D image segmentation [46], its 3D counterpart [49], and the V-Net model [56]. Compared to U-Net, V-Net employs down-convolutions instead of max pooling and residual connections inside the encoding or decoding path. Applications of 3D V-Net include prostate segmentation [56] and vertebrae segmentation [58]. A 2.5D variant of V-Net has been successfully applied for liver and vessels' segmentation [14; 59] and lung parenchyma, together with COVID-19 lesions' segmentation [22].  
In this work, we implemented the 2.5D architecture, but in a multi-class classification fashion, with a different loss function (focal Dice loss). The scheme of the employed architecture is depicted in Figure 2.  
For the liver segments' classification task, we randomly sampled 2.5D, i.e., 2D multi-channel, patches composed of 5 slices from volumes of the training set, as performed in [PERSON] et al. [14]. We trained the V-Net model for 1000 epochs, for each of the proposed loss function configurations reported in Section 3. The learning rate started from 0.01 and was  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class** & **Voxels** & **Frequency \(f_{k}\) (\%)** & \(\frac{1}{f_{k}}\) & \(\log(\frac{1}{f_{k}})\) \\ \hline Background & 2,561,653,684 & 95.35 & 1.05 & 0.0476 \\ \hline Segment I & 5,653,635 & 0.21 & 475.17 & 6.1637 \\ \hline Segment II & 15,076,888 & 0.56 & 178.18 & 5.1828 \\ \hline Segment III & 8,858,869 & 0.33 & 303.25 & 5.7146 \\ \hline Segment IV & 14,139,614 & 0.53 & 189.99 & 5.2470 \\ \hline Segment V & 16,275,115 & 0.61 & 165.07 & 5.1063 \\ \hline Segment VI & 15,679,458 & 0.58 & 171.34 & 5.1436 \\ \hline Segment VII & 23,566,210 & 0.88 & 114.00 & 4.7362 \\ \hline Segment VIII & 25,548,239 & 0.95 & 105.15 & 4.6554 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Class distribution for the selected training set (n=168) from MSD 08 dataset.  
Figure 2: V-Net for multi-class segments’ classification.  
reduced by a factor of 10 every 166 epochs. The [PERSON] optimizer [60], with a batch size of 4, was used to carry out the training process.