esa-sceva/satcom-chunk-collection huggingface dataset:
# A Cloud Based Disaster Management System
## 4 Extending the RPL Protocol with Cyber-Physical Objective Function
Figure 4 sums up the operations of Cyber-OF. First, the OF based on the energy metric is activated. RPL routing will consider this OF until a critical event (e.g., fire) is detected. This means that every router chooses a parent that will optimize the energy metric in the case of normal operation. When a critical event is detected in a packet en route, RPL automatically switches to the latency OF, which allows it to find reliable minimum-latency paths. The measure utilized by Cyber-OF is decided bythe metrics found within the DIO metric container. Thus, the route from the node to the gateway router will change so as to reduce the end-to-end delay, rather than focusing on energy, considering the criticality of the event to be transported. Thus, in ordinary circumstances, the OF increases the system lifespan. In emergencies where serious events are discovered, a node required in advancing this process to the border router must utilize a new OF that minimizes end-to-end delays. It is clear that the design of Cyber-OF allows for consideration of the cyber-physical properties of the environment, enabling network topologies to adapt to these conditions. In fact, when a high temperature is detected, the routers understand that this event is critical and must switch the routing strategy to meet the application real-time requirements. In our design of Cyber-OF, we considered the energy metric for normal situations and the delay metric for critical situations. As these two metrics are only representative, other metrics that would better represent each situation can be considered instead. In the fire disaster context, we can identify several unexpected behaviors. Early detection of fires in cities and urban areas is essential in order to prevent more losses and the spread of fire. All of these emergency situations require early detection to provide an alarm in real-time. This is the main reason why we proposed using end-to-end delay as a routing metric in the case of a critical event. From the implementation perspective, the end-to-end delay metric represents the sum of link latencies for the path of RPL routers traversed by the packet. Thus, every node in the network should maintain a link latency measure to all its neighbor nodes, which will be used to select the next router in the path for every packet. The next-hop selected is the RPL router that has the lowest latency. This strategy will be used the latency-OF method that improves the real-time performance in critical situations. Regarding the energy metric, every RPL router estimates its remaining battery level. The next RPL router selected in the path is the router that has the highest remaining energy. This strategy will be used the energy-OF method that improves the network lifetime performance in normal situations.
# Nonlinear Dynamics of a Resonant-Impact Dielectric Elastomer Actuator
## 1 Introduction
[36] proposed a vibro-impact self-propulsion capsule endoscope for small-bowel examination, which was able to achieve a forward velocity of 8.49 mm/s. However, it is worth noting that existing vibro-impact systems mainly rely on rigid actuators such as servomotors or solenoid coils to generate impact forces. These rigid and bulky actuators severely limit the adaptability of robotic systems in unstructured environments, and may raise potential risks during human-robot interactions. Therefore, utilizing a vibro-impact mechanism and a DEA, we proposed a soft actuator-based resonant-impact DEA system in our previous study [37]. This system mainly consists of a resonant DEA and an elastic constraint as the output to any payloads. This design separates the resonant DEA from the payloads, thus minimizing the negative effects of high payload damping on its resonant strokes. Meanwhile, the elastic constraint transfers the impact force to the payload effectively. The resonant-impact DEA system combines the advantages of the high instantaneous force/power output of a vibro-impact mechanism and the high energy efficiency and stroke of a resonant DEA. A novel crawling robot was developed based on this mechanism, and its crawling locomotion was demonstrated on various surfaces with different friction coefficients. However, the previous work was limited solely to experimental analyses. Despite nonlinear dynamic responses being characterized briefly in our experiments, no model of the resonant-impact DEA system is available to perform in-depth theoretical investigations or optimization due to the multiple nonlinearities and unique electromechanically coupled forcing mechanism of the DEAs. In this paper, we first develop a nonlinear dynamic model by considering the structural, material, and constraint nonlinearities and electromechanical coupling of the DEA. Utilizing the developed model, we investigate the nonlinear dynamics of the resonant-impact DEA system in depth through extensive experiments. This paper characterizes the effects of key parameters such as the excitation voltage amplitude, constraint gap, constraint stiffness, and the number of DEA layers on the dynamic responses of the system. The study of the nonlinear dynamics of the resonant-impact DEA system provides guidance for the design and performance optimization of systems and their applications. The rest of this paper is organized as follows. Section 2 introduces the design, working principle, and numerical model of the resonant-impact DEA system. Our comparative analyses of the numerical and experimental results of different design parameters are characterized in Section 3. Finally, our conclusions are presented in Section 4.
## 5 Results and Discussion
### Electricity Production  
Figure 11 shows the rainfall at Ricon de Soto (Spain) for the years under study.  
In addition to the level of rainfall, the need for water for irrigation in each year should be taken into account. Taking into account that the area of land used for cultivation has not changed and that the type of crop has not changed either, the water needs for irrigation in the four years can be considered to be similar and allow for an acceptable comparison.  
Figure 12 shows the monthly energy generated by Rebolluelo micro-hydropower plant with fixed-speed technology during 2016 and 2017 [34].  
As can be seen in Figure 12, since March 2017 the Rebolluelo micro-hydropower plant has not produced any electricity. The semi-Kaplan turbine broke down due to severe cavitation problems.  
Figure 13 shows the monthly energy generated by the Rebolluelo micro-hydropower plant with variable speed technology during 2021 and 2022 [34]. Even when using variable-speed technology, the micro-hydropower plant was not operational during some months of the year due to the low flow available. This situation would have been even worse if fixed-speed technology were used.  
Figure 14 shows the annual power generated by the Rebolluelo micro-hydropower plant with both technologies. The micro-hydropower plant operated with fixed speed technology in 2016 and 2017, and it operated with variable-speed technology in 2021 and 2022.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Cost Type** & **Cost (f)** & **(\%)** & **Reference** \\ \hline Civil costs & 112,430.64 & 20.33 & [34] \\ Electro mechanical equipment costs & 378,765.37 & 68.51 & [34] \\ Power transmission line costs & 47,302.30 & 8.55 & [34] \\ Indirect costs & 14,382.59 & 2.61 & [34] \\ \hline \hline \end{tabular}
\end{table}
Table 4: The cost of the Rebolluelo micro-hydropower plant with variable-speed technology.  
Figure 11: Rainfall at Ricón de soto (Spain) for the years under study.  
Figure 12: Monthly energy generated by the Rebolluelo micro-hydropower plant with fixed-speed technology.  
Figure 13: Monthly energy generated by the Rebolluelo micro-hydropower plant with variable-speed technology.  
The year 2016 was used as the basis for comparison in order to determine the _POR_. Compared with 2016, the power output ratio increased by 93.03% and 35.87% in 2021 and 2022, respectively. Therefore, the implementation of variable-speed control significantly increased the generation of electrical energy.
###### Abstract  
With the prevalence of cloud computing, the outsourcing of computation has gained significant attention. Clients with limited computing power often outsource complex computing tasks to the cloud to save on computing resources and costs. In outsourcing the computation of functions, a function owner delegates a cloud server to perform the function's computation on the input received from the user. There are three primary security concerns associated with this process: protecting function privacy for the function owner, protecting input privacy for the user and guaranteeing that the cloud server performs the computation correctly. Existing works have only addressed privately verifiable outsourcing computation with privacy or publicly verifiable outsourcing computation without input privacy or function privacy. By using the technologies of homomorphic encryption, proxy re-encryption and verifiable computation, we propose the first publicly verifiable outsourcing computation scheme that achieves both input privacy and function privacy for matrix functions, which can be extended to arbitrary multivariate polynomial functions. We additionally provide a faster privately verifiable method. Moreover, the function owner retains control over the function.  
outsourcing computation; function privacy; input privacy; publicly verifiable computation; homomorphic encryption; proxy re-encryption Article
# Deep Learning-Based Estimation of Muckpile Fragmentation Using Simulated 3D Point Cloud Data
## 3 Methodology
### Experimental Procedures  
In this study, we propose a method to estimate the particle size distribution of muckpile using an improved PointNet network, which is a deep learning technique that reconstructs the 3D point cloud of muckpile and acquires features from local shapes. This method consists of two phases: the learning phase and the inference phase. In the learning phase, simulation is used to create training data. The processing flow is shown in Figure 7. After generating the CG model of the muckpile using the simulation, multi-view images are obtained with a virtual camera, and point cloud data is obtained using SfM. In addition, the true particle size distribution is measured and used as the dataset for training the particle size distribution estimation. Since we are using a CG model of rocks with known sizes in this study, it is possible to measure the particle size distribution of the muckpile CG model. After training the particle size distribution estimation network using this dataset, in the inference phase, the actual muckpile is photographed and point cloud reconstruction is performed. The particle size distribution is then estimated by inputting the reconstructed point cloud into the trained network.  
Figure 6: T-Net Functions.  
Figure 7: Flow of the Experiment.  
The particle size distribution of muckpile is represented by the cumulative distribution shown in Figure 8, with the vertical axis being the cumulative undersize value and the horizontal axis being the particle size. The cumulative undersize distribution is independent of the interval setting, unlike the frequency distribution, so anyone can obtain the same distribution when tallying the data. Moreover, the proportion of particle sizes is represented by the slope of the graph. In the case of Figure 8a, the gentle slope indicates the presence of particles with a wide range of sizes. On the other hand, Figure 8b shows that particles around 800 mm are most abundant due to the steep slope. In the mining field, it is important for the particle size distribution of muckpile to have a certain particle size, as shown in Figure 8c.
## 5 Discussion
### Strength, Limitation, and Potential Future Application of the Study
The availability of our method to create a segmentation mask will also make it possible for further DL research in CLRM tumors within the MRI modality. Starting with a 3D segmentation by our DL method, most of the liver is already segmented. Therefore, only detecting and correcting the most significant over- and under-segmentation is left in completing the segmentation. An extension of the dataset, and training the method on more samples, will make the method more robust and increase the sensitivity of the method. Lesion segmentations produced by the presented method still require careful evaluation by a medical expert for its classification, and border adjustment. Even false-positive predictions will draw medical experts' attention to the specific location, which might have an atypical pattern or might even be a missed lesion. Our DL method was trained on the GT provided by just two medical experts, which can result in overfitting to their original segmentation of the MRI data. Between medical experts, the segmentation of the same structure will never have a 100% overlap due to individual human visual perception. Our future plan is to expand the study and involve more medical experts, to make the GT less biased and also to expand the method for other types of liver tumors. Though our GUI can be used to create fast 3D segmentation for the liver parenchyma and tumors for an unannotated dataset (test set #2), the method is still limited by the type of machine on which it is trained. During the past years, 3D Slicer made several artificial intelligence (AI)-based application tools to speed up the process of medical image segmentation (AI-Assisted Annotation Server from NVIDIA [57], MONAI label from MONAI [58]). They require minimal manual initialization and show good segmentation results on CT images for liver and liver tumor segmentation tasks. In the future, we aim to integrate our trained method into this solution and make our method publicly available to increase the usability of MRI images by them.
## 3 System Model
### Physical Layer Model  
We consider the Rayleigh fading channel model which is a discrete time block with additive white Gauss noise. Its power spectrum density is \(N_{0}/2\).The wireless channel bandwidth is \(W\). In this paper, we use a Finite State Markov Channel (FSMC) in order to describe the wireless channel. As shown in Figure 3, there are \(k\) channel states and each can be transfered to its adjacent states with corresponding probabilities.  
The signal-to-noise ratio (SNR) \(\varphi\) is received instantaneously and exponentially distributed in the Rayleigh fading channel. The probability density function is:  
\[f_{\varphi}(\varphi)=\varphi_{0}\exp(-\frac{\varphi}{\varphi_{0}}) \tag{1}\]  
where \(\varphi_{0}=\mathrm{E}[\varphi]\) is the average gain of the channel. If the received SNR is in the interval \([\varphi_{k},\varphi_{k+1}]\), the channel is in state \(\mathrm{h}_{k}\). N(\(\varphi\)) is the level crossing rate (LCR). It is given by:  
\[N(\varphi)=\sqrt{\frac{2\pi\varphi}{\varphi_{0}}}\cdot f_{d}\cdot\exp(-\frac{ \varphi}{\varphi_{0}}) \tag{2}\]  
where, \(f_{d}\) is the maximum Doppler frequency. Hence, the state transition probability can be obtained by the following formula:  
\[\begin{cases}p_{\pm}(k,k+1)=N(\varphi_{\pm+1})\cdot\Delta t/\pi_{k}&,1\leq k \leq K-1\\ p_{\pm}(k,k-1)=N(\varphi_{k})\cdot\Delta t/\pi_{k}&,2\leq k\leq K\\ p_{\pm}(k,k)=1-p_{\pm}(k,k+1)&,k=1\\ p_{\pm}(k,k)=1-p_{\pm}(k,k-1)&,k=K\\ p_{\pm}(k,k)=1-p_{\pm}(k,k+1)-p_{\pm}(k,k-1)&,k\
eq 1,K\end{cases} \tag{3}\]  
where, the steady state probability \(\pi_{k}\) is given by:  
\[\pi_{k}=\int_{\varphi_{k}}^{\varphi_{\pm+1}}f_{\varphi}(\varphi)d\varphi=\exp (-\frac{\varphi_{k}}{\varphi_{0}})-\exp(-\frac{\varphi_{k+1}}{\varphi_{0}}) \tag{4}\]
## 3 Materials and Methods
### Tilt Data  
On Mt. Etna, surface local inclination variations are continuously monitored by using instruments able to detect tilt changes with high-precision data.  
In December 2015, the Mt. Etna permanent tilt network consisted of 14 borehole instruments and a long-base device (PDN, Figure 3). Over the last ten years, INGV improved the network through the installation of sensors at greater depth: currently, eight stations are at 27-30 m depth and six are 10 m deep, including three summit stations.  
The deeper stations use biaxial self-leveling instruments with very high-precision (10\({}^{-8}\)-10\({}^{-9}\) radians) electrolytic bubble sensors for measuring the angular movement and magnetic compasses that are able to detect tides. Almost all stations have a sampling rate of 1 min. A description of the tilt instrumental characteristics is provided by [7; 8; 9].  
Continuous tiltmeters recorded variations during the four fountain episodes occurring on 3-5 December 2015. Tilt changes during the first, most powerful lava fountain were recorded at all the stations showing values comprising a few tenths to a couple of microradians with a mean value of 1.1 (Figure 4). The magnitude of episode II was slightly  
Figure 3: Map of the permanent tilt and GNSS networks operating on Mt. Etna.  
lower (mean value of 0.85 microrads), while the successive episodes had a significantly smaller amplitude (mean values, respectively, of 0.5 and 0.35 microradians).  
The first change occurred on December 3 at about 02:30 UTC and all the four episodes showed a mean duration of about 1 h. The pattern of the ground tilt was the same for each event, even if its magnitude decreased from the first to last episode, evidencing similar sources (Figure 4). At the end of the entire sequence, the tilt showed the same trends as before the fountains.  
The tilt vectors showed a general lowering toward an area positioned just west of the Mt. Etna summit (Figure 5), suggesting a repeated occurrence of deflation processes.  
Figure 4: Tilt components recorded at several stations during the fountain episodes. Grey lines highlight the 4 paroxysms evidencing associated changes. Radial (Rad) components are directed toward summit craters and positive variations indicate an upward tilt. Oriented components (e.g., CBD N220\({}^{\circ}\)E) indicate a downward tilt along the indicated direction.
## 2 Results and Discussion
### Fourier Transform Infrared (FT-IR) Analysis  
Figure 4 shows the FT-IR spectra of MPMs and EDA-MPMs. The typical peak of Fe-O bond appeared at 540 cm\({}^{-1}\) for the spectra in Figure 4a,b, which proved that the Fe\({}_{3}\)O\({}_{4}\) nanoparticles remained in the polymer. As shown in Figure 4a, the strong band at 1700 cm\({}^{-1}\) corresponded to C=O vibrations, and the bands at 855 and 972 cm\({}^{-1}\) were attributed to the epoxy groups on the MPMs. After ring opening reaction with EDA in Figure 3b, the bands at 855 and 972 cm\({}^{-1}\) disappeared, and new bands appeared at 1570 and 3291 cm\({}^{-1}\), which were corresponding to the N-H vibrations in the EDA. These results clearly indicated that the ester groups have successfully reacted with EDA on the MPMs. In addition, the higher nitrogen amount (7.01%) found in EDA-MPMs confirmed that a polymer shell had been grafted with amino groups by elemental analysis.  
Figure 3: XRD spectrum of (**a**) Fe\({}_{3}\)O\({}_{4}\) nanoparticles; (**b**) MPMs and (**c**) EDA-MPMs.
# Secure K-Means Clustering Scheme for Confidential Data Based on Paillier Cryptosystem
## 3 Preliminaries
### K-Means Clustering  
The K-means clustering algorithm such as Algorithm 1 is described as follows.  
Initialization: Randomly select \(K\) data points as initial cluster centers.  
Iterative Optimization: Iteratively update data point assignments and cluster centers until convergence or reaching the maximum number of iterations.  
Cluster Identification: Assign data points to the nearest cluster center.  
Selecting the Optimal \(K\): Use methods such as the elbow method or silhouette score to select the optimal number of clusters.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Description** \\ \hline \(E(x_{i})=\{E(x_{ik})\}_{k=1}^{d}\) & Encrypted values of each dimension of data point \(x_{i}\) \\ \(E(c_{j})=\{E(c_{jk})\}_{k=1}^{d}\) & Encrypted values of each dimension of cluster center \(c_{j}\) \\ \(E(d_{ij}^{2})\) & Encrypted squared Euclidean distance between data point \(x_{i}\) and cluster center \(c_{j}\) \\ \(E(\Delta)\) & Encrypted difference for each dimension \(E(x_{ik}-c_{jk})\) \\ \(E(\Delta^{2})\) & Encrypted squared difference for each dimension \(E((x_{ik}-c_{jk})^{2})\) \\ \hline \(\{E(d_{ij}^{2})\}_{j=1}^{K}\) & Encrypted squared distances from data point \(x_{i}\) to each cluster center \(c_{j}\) \\ \(E(\lambda_{i})\) & Encrypted index of the cluster center to which data point \(x_{i}\) is assigned \\ \(E(C_{j})\) & Encrypted cluster set for cluster center \(c_{j}\) \\ \(E(C_{\lambda_{i}})\) & Encrypted cluster to which data point \(x_{i}\) is assigned \\ \(U\) & Set of encrypted distances used for finding the minimum \\ \(s\) & Number of remaining pairs of encrypted distances after one round of comparison \\ \hline \(\{E(x_{i})\}_{i=1}^{N}\) & Set of encrypted data points \\ \(\{E(c_{j})\}_{j=1}^{K}\) & Set of encrypted cluster centers \\ \(\{E(c_{j}^{*})\}_{j=1}^{K}\) & Set of updated encrypted cluster centers \\ \(E(c_{j}^{*})\) & Encrypted accumulator for cluster center \(c_{j}\) \\ \(|C_{j}|\) & Number of data points in cluster \(C_{j}\) \\ \(E(|C_{j}|)\) & Encrypted number of data points in cluster \(C_{j}\) \\ \(E(b_{ij})\) & Encrypted indicator whether data point \(x_{i}\) belongs to cluster \(C_{j}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notations.  
```
1:Input: Dataset \(X=\{x_{1},x_{2},...,x_{n}\}\) and number of clusters \(K\)
2:Output: Cluster assignments \(c_{i}\) and cluster centers \(\mu_{j}\)
3:Randomly initialize \(K\) cluster centers \(\mu_{1},\mu_{2},...,\mu_{K}\)
4:repeat
5:for each data point \(x_{i}\)do
6: Assign \(x_{i}\) to the nearest cluster center: \(c_{i}\leftarrow\text{argmin}_{j}||x_{i}-\mu_{j}||^{2}\)
7:endfor
8:for each cluster center \(\mu_{j}\)do
9: Update \(\mu_{j}\) to be the mean of the points assigned to cluster \(j\)
10:endfor
11:until convergence
12:Return Cluster assignments \(c_{i}\) and cluster centers \(\mu_{j}\)
```  
**Algorithm 1** K-means Clustering