esa-sceva/satcom-chunk-collection huggingface dataset:
# Formal Modeling of IoT-Based Distribution Management System for Smart Grids
## 3 Problem Statement and System Model
### Advanced Metering Infrastructure (AMI)  
The primary objectives of the smart grid are: self-coordination, self-awareness, self-healing, and self-reconfiguration, to add intelligence to the grid so that it can perform, to increase the deployment of renewable energy sources, to improve the efficiency of power generation, transmission, and usage, and to shift and configure consumers' energy demands by using demand response (DR) techniques to manage peak loads of customers. Sophisticated distribution automation and price optimization models based on automated meter reading (AMR) and advanced metering infrastructure are required to achieve this. Smart meters are similar to a traditional electric meter but with enhanced ICT-enabled features because they not solely measure the amount of energy consumed but also track a vast amount of data over time, such as the patterns of electricity usage. AMI uses smart control and communication technologies to automate metering services that were previously done by hand, which are time-consuming activities such as energy meter readings, service connection and disconnection, interventions and theft detection, the monitoring of voltage, and fault and outage identification, when combined with cutting-edge customer-centric technology. The Drive-by/Walk-by meter reading in AMI is depicted in Figure 2.
## 4 Results of the Sensitivity Analysis to Setup Parameters  
### Deterministic Results  
The effects of the variation of \(SV\), \(T\), and \(L/D\) are first analyzed in terms of flow-rate waveforms at the outlet sections (BCA, LCCA, LSA, and DA) and of the differential pressure waveforms, \(\Delta p(t)\), evaluated between the ascending (\(\Gamma_{\text{AA}}\)) and the descending (\(\Gamma_{\text{DA}}\)) aorta as follows:  
\[\Delta p(t)=p_{\Gamma_{\text{AA}}}(t)-p_{\Gamma_{\text{DA}}}(t) \tag{6}\]  
Figure 9 shows the time behavior of the flow rates at the four outlet sections of the healthy aorta in the 64 in vitro measurements. It is clear that \(SV\) and \(T\) affect the flow-rate magnitude, which increases by increasing \(SV\) and/or reducing \(T\), but slight differences are found among the shapes of the waveforms. The length \(L\) does not significantly influence the waveforms. The same trends also hold for the diseased geometries (Figures 10 and 11). For the aneurismatic aorta, the flow repartition between DA and the supra-aortic branches and the related flow-rate waveforms are also similar to the ones of the heathy geometry (compare Figure 10 with Figure 9), and again, \(SV\) and \(T\) are the main ones responsible for waveform variations. Despite the presence of the coarctation leading to a flow-rate reduction in the descending aorta for the 64 experiments presented in Figure 11, the main parameters influencing the waveform magnitude are still the same.  
Figure 9: Effect of \(SV\), \(T\), and \(L/D\) on the deterministic waveforms of the flow rate at the four outlets of the healthy aorta: (**a**) DA, (**b**) BCA, (**c**) LCCA, and (**d**) LSA.  
As for the differential pressure waveforms, it is evident from Figure 12 that this quantity is massively influenced by the presence of the coarctation. For a given triple (\(T\), \(SV\), and \(L/D\)), the pressure drop increase of about one order of magnitude in the case of coarctation compared with healthy and aneurysmatic geometries gives lower and similar results in terms of waveforms. This is due to the pressure drop occurring in the coactation region, and we recall that the guideline for coarctation treatment recommends to be more accurate than \(10^{-5}\).  
Figure 11: Effect of \(SV\), \(T\), and \(L/D\) on the deterministic waveforms of the flow rate at the four outlets of the coarcated aorta: (**a**) DA, (**b**) BCA, (**c**) LCCA, and (**d**) LSA.  
Figure 10: Effect of \(SV\), \(T\), and \(L/D\) on the deterministic waveforms of the flow rate at the four outlets of the aneurysmatic aorta: (**a**) DA, (**b**) BCA, (**c**) LCCA, and (**d**) LSA.  
surgical restoration for \(\Delta p\geq 20\) mmHg at rest [59]. For each geometry, stroke volume and cardiac cycle period significantly impact the magnitude of the waveforms, whereas the inlet velocity profiles, i.e., of the lengths \(L/D\), have a negligible effect.
# A Fast Point Clouds Registration Algorithm for Laser Scanners
## 2 Proposed Method  
The flow chart diagram of the proposed method is shown in Figure 1, which mainly consists of five steps. First of all, the voxel grid filter is used to uniformly sample the point cloud, reducing the registration time. Then, the feature points are extracted from the down-sampled point cloud by the ISS algorithm. Next, the 3 DSC algorithm is used to describe the extracted feature points to form the ISS-3 DSC features. After that, the ISS-3 DSC feature is used to coarse registration by RANSAC algorithm. Finally, the ICP algorithm is used for fine registration. For high efficiency, k-dimensional (KD) tree algorithm is used to accelerate this process.
## 4 Results and Discussion
### Node Criticality Analysis  
The PageRank algorithm obtains the criticality of all the seafarers' unsafe acts and their influencing factors in the developed interaction network, as shown in Figure 9. Table 3 lists the top 10 and bottom 10 important nodes in the network. Failure to make proper sound and light signals (U16) has the highest PageRank value, followed by failure to take effective collision avoidance action early (U25), and improper emergency response measures (U20). It can be seen that the top 10 nodes all belong to seafarers' unsafe acts, which confirms that seafarers' unsafe acts are the direct causes of maritime accidents, and effective control of seafarers' unsafe acts with higher importance could interrupt the propagation of risks and thus reduce the occurrence of maritime accidents. On the other hand, most of the nodes in the bottom 10 are influencing factors, except for \"Failure to keep navigational equipment in working state (U3)\", which belongs to seafarers' unsafe acts. In addition, five of the remaining nine influencing factors are external environmental factors, which indicate that risk management of such external objective influencing factors may have little effect.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**The Top 10** & & & \multicolumn{2}{c}{**The Bottom 10**} \\ \hline
**Rank** & **Nodes** & **PR** & **Rank** & **Nodes** & **PR** \\ \hline \multirow{2}{*}{1} & Failure to make proper sound and & & & & \\  & light signals (U16) & 0.1369 & 53 & Failure to keep navigational & \\  & light signals (U16) & 0.1369 & 53 & equipment on working state (U3) & \\  & Failure to take effective collision & & & Poor communication & \\
2 & avoidance action early (U25) & 0.0802 & 52 & (ship-shore) (P2) & \\  & Improper emergency response & & & & \\
3 & measures (U20) & 0.0563 & 51 & Lack of standardization (O7) & \\
4 & Failure to determine the risk (U31) & 0.0545 & 50 & Lack of operation certificate (O2) & \\
5 & Unsafe speed (U24) & 0.0478 & 49 & Insufficient device (O1) & \\  & Failure to maintain proper & & & \\  & lookout (U17) & 0.0357 & 48 & External management (E8) & \\
7 & Failure to follow the rules in sight of & & & \\  & one another (U27) & 0.0329 & 47 & Poor visibility (E5) & \\
8 & Failure to follow the rules in special & & & \\  & waters such as narrow channels (U26) & 0.0296 & 46 & Busy traffic (E4) & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Top 10 and bottom 10 nodes ranked in criticality.  
Figure 8: Betweenness centrality of the nodes.  
Figure 9: Ranking of the nodes’ PageRank value.
# Improved ECG Watermarking Technique Using Curvelet Transform
## 4 Data Extraction  
A reversible blind extraction process is presented in this section that mined an embedded watermark from the ECG signals. In this work, the original watermark is not required to extract of the patient's information. The ECG embedded with patient's information i.e., watermarked ECG is firstly converted to 2D-ECG signal as the procedure discussed in Section 3.1. To convert time-domain image into frequency domain curvelet transform was applied on 2-D watermarked ECG image and then select course level of curvelet transform. The selected scale's coefficients are classified into clusters as discussed in Section 3.4 above. To extract the embedded watermark, use the embedding clusters, i.e., \(\rm{R_{(X-1)}}\) to \(\rm{R_{-(X-1)}}\). Then for each selected cluster calculate \(\rm{l_{0}}(q),\ \rm{l_{1}}(q),\ \rm{L_{0}}(q),\ \rm{L_{1}}(q)\) by using \(\rm{Radi_{i1}}\), \(\rm{Radi_{i2}}\) as discussed in Equations (5)-(7). Then for each selected cluster calculate extract bits of the watermark by using the below equation.  
\[\left\{\begin{array}{c}w=1\ \rm{if}\ (\rm{L_{0}}\leq D\leq L_{1})\wedge(A\% 2==1)\\ w=1\ \rm{if}\ (\rm{(l_{0}}\leq D\leq l_{1})\wedge(A\% 2==1)\\ w=1\ \rm{Otherwise}\end{array}\right. \tag{10}\]
# Exploiting the Capture Effect to Enhance RACH Performance in Cellular-Based M2M Communications
## 4 Analysis Model
### RA Throughput
#### 4.2.2 Steady-State RA Throughput Analysis Model  
As seen in Equation (14), the capture probability, \(CP_{N,m,r}\), is affected by the distribution of the ramping number \(\mathbb{P}(R_{u}|N)\), which is determined by \(E[N_{T=t}^{k}]\) as in Equation (9). Therefore, \(E[N_{T=t}^{k}]\) must first be obtained. Let us assume that the network is in a steady-state with a stable number of RA accesses \(N\) at each RA slot; then, with a slight abuse of notation, we can still represent \(N\) and \(N_{T=t}^{k}\) as steady-state values. Then, the following condition holds.  
Note that \(E[N_{T=1}]=\sum_{k}E[N_{T=1}^{k}]\) denotes the number of new arrivals. Now, we introduce a Markov chain model to determine the state of the devices, where state \(<t,k>\) presents those devices with a trial number \(t\) and are located in _zone_\(k\). If we assume that the M2M devices have no mobility, a state transition only occurs when a device (a) fails in RA and retries (from \(<t,k>\) to \(<t+1,k>\)), (b) succeeds in RA, or (c) fails in its connection set up. Figure 2 presents this Markov chain model. Then, \(\mathbb{P}_{<t,t+1>}^{k}\) can be derived as follows:  
\[\mathbb{P}_{<t,t+1>}^{k} =1-\mathbb{P}(S_{u}|N,T_{u}=t,Z_{u}=k)\] \[=1-\mathbb{P}(S_{u}|N,R_{u}=\min(t,k)) \tag{19}\] \[\qquad t\in[1,N_{max}-1],\quad k\in[1,N_{max}],\]  
where  
\[\mathbb{P}(S_{u}|N,R_{u}=r) =\frac{\mathbb{P}(S_{u},R_{u}=r|N)}{\mathbb{P}(R_{u}=r|N)} \tag{20}\] \[=\sum_{m=0}^{N-1}\binom{N-1}{m}\left(\frac{1}{V}\right)^{m}\left( 1-\frac{1}{V}\right)^{N-m-1}\!\!CP_{N,m,r}.\]  
Finally, we can obtain the steady-state value \(E[N_{T=t}^{k}]\) using balance equation property.  
\[E[n_{T=t+1}^{k}]=\left\{\begin{array}{lcl}\mathbb{P}(Z_{u}=k)E[N_{T=1}]&,&t= 1,\\ \mathbb{P}_{<t,t+1>}^{k}E[n_{T=t}^{k}]&,&1<t\leq N_{max}-1,\end{array}\right. \tag{21}\]  
\[\sum_{t=1}^{N_{max}}\sum_{k=1}^{N_{max}}E[N_{T=t}^{k}]=N. \tag{22}\]  
Here, the first line of Equation (21) holds because the locations of devices are uniformly distributed. From Equations (21) and (22), \(E[N_{T=t}^{k}],\forall t,k\) can be obtained. In summary, the steady-state RA throughput for fixed the number of accesses, \(N\), can be obtained by the iterative procedure as in Figure 3.
## 2 Materials and Methods
### Policy Gradient-Based Deep Reinforcement Learning
#### 2.3.2 Policy Gradient  
Let us consider a reinforcement learning problem with a policy \(\pi(a|s,\theta)\), where \(\pi\) represents a probability distribution over actions \(a\), given state \(s\), and \(\theta\) denotes the policy parameters. The objective function we aim to maximize is the expected cumulative reward, which can be expressed as follows:  
\[J(\theta)=E\Big{[}\sum_{t=0}^{T}\gamma^{t}R_{t}\big{|}\pi\Big{]} \tag{1}\]  
where \(\gamma\) is the discount factor, \(R_{t}\) is the reward at timestep \(t\), and \(T\) is the time horizon.  
To maximize the \(J(\theta)\), we need to find the gradient of the objective function with respect to the policy parameters \((\theta)\). Using the likelihood ratio trick, we can rewrite the gradient as follows:  
\[\
abla J(\theta)=\frac{\partial J(\theta)}{\partial\theta}=E\Big{[}\sum_{t=0} ^{T}\frac{\partial log\pi(a_{t}|s_{t},\theta)}{\partial\theta}*\gamma^{t}R_{ t}\Big{]} \tag{2}\]  
The expectation is the above formula that can be estimated using Monte Carlo sampling. By collecting a set of trajectories \(\tau=[a_{0},s_{0},a_{1},s_{1},\cdots,a_{T},s_{T}]\) using the current policy \(\pi\), we can approximate the expectation as follows:  
\[\
abla J(\theta)\approx\frac{1}{N}\sum_{i=1}^{N}\sum_{t=0}^{T}\frac{\partial log \pi(a_{t}|s_{t},\theta)}{\partial\theta}*\gamma^{t}R_{t}^{i} \tag{3}\]  
where \(N\) is the number of trajectories sampled, and \(\Big{(}a_{t}^{i},s_{t}^{i},R_{t}^{i}\Big{)}\) represents the state, action, and reward at timestep \(t\) in the \(i\)-th trajectory.  
This approximation is unbiased because the expectation of the Monte Carlo estimate is equal to the true gradient. In other words, as the number of the sampled trajectories \(N\) approaches infinity, the estimate converges to the actual policy gradient, as follows:  
\[E\Big{[}\frac{\partial J(\theta)}{\partial\theta}\Big{]}=\frac{\partial J( \theta)}{\partial\theta} \tag{4}\]  
In conclusion, the policy gradient method optimizes the policy parameters by computing the gradient of the expected cumulative reward with respect to the policy parameters.  
Figure 7: Policy network.  
Monte Carlo sampling is employed to estimate the gradient, providing an unbiased estimate of the true gradient as the number of sampled trajectories increases.
## 5 Proactive Services and Alert Generation  
Exploiting the dynamic risk map described in the section above, it is possible to configure a collection of proactive services for 24/7 monitoring of the areas of interest in order to automatically issue alerts in case anomalies are detected.  
The main goal has been to infer from the heterogeneous collected data, which are a mixture of authoritative and non-authoritative information, events and trends of interest. From an architectural point of view, we decided to use a collection of services, each of them being implemented by an intelligent software agent. Agents work in autonomy and are independently reconfigurable to a large extent. Each of them has a number of probes for fetching data from the MIS and a set of preconfigured actions. Each one is endowed with an inner logic, which represents the workflow that it is committed to carry out. Each step in the workflow is represented as a condition-action rule. The workflow might include the acquisition of further data or the triggering of external computational methods (e.g., for running simulation or assessing risk again). Agents activate their probes at regular interval of times (e.g., they poll data from a repository) or they are triggered on demand (e.g., by other agents or upon reception of special requests or new data). A number of agents, each one, implementing alerting services were designed and developed. As described in the previous section, the real-time risk estimation offers a synthetic parameter blending all the available information in a certain area. It is therefore natural to configure an agent dedicated to the observation of estimated risk and to the generation of alerts in case unexpected increases occur. Another agent that has been configured is based solely on the analysis of the collected VGI. In particular, this agent applies spatial and temporal aggregation on the set of received volunteers' alerts and detects the presence of significant clusters. In case one is found, an alert is generated with variable grades of severity. Besides the mere generation of an alert message, agents take care of contacting the most suitable authority selected automatically on the basis of a proximity criterion. A message is composed including all the information that might be useful for understanding and evaluating the alert. In particular, the location the alert refers to, the observations that triggered the event and a link to an ad hoc resource of the MIS web interface are provided. Finally, the service connects through specific Application Programming Interfaces (APIs) with the actual dispatching interfaces (e.g., either email server or the mobile extension of the MIS for SMS).
# Transparent Redirections in Content Delivery Networks
## 4 Verification
### Request Router Implementation  
The request router is responsible for handling the redirections. Although we use a transparent redirection mechanism which is mostly handled by the SDN controller, we implemented a request router to be able to fully handle TCP connections and HTTP messages. The request router is implemented in NodeJS due to its robustness and performance [35].  
The request router consists of 4 main modules:  
* Http endpoint module: this module opens a TCP socket, which listens for incoming HTTP requests. The endpoint is running on port 8082. If a request is received, it is parsed and then prepared for handoff.
* Controller endpoint connector module: this module is responsible for communicating with the SDN controller's Northbound API over the WebSocket based JSON-RPC endpoint.
* Request router module: this module is responsible for handling the CDN logic. The module stores information about the assets and maintains the pool of established TCP connections toward the surrogate servers. On incoming requests, this module is responsible for modifying the request and sending it out to the surrogate server. The surrogate server selection is done by the SDN controller, which is queried by the controller endpoint connector module.
* API endpoint: this module provides a REST API for the GUI. The REST API listens on port 3000.  
The request router implementation is available on github ([[https://github.com/rf6](https://github.com/rf6) nco/reqNRouter.git]([https://github.com/rf6](https://github.com/rf6) nco/reqNRouter.git)).
## 4 Results and Discussion
### Raman Scattering
#### 4.1.1 Polarization-Dependent Raman Spectra  
In Figure 3a-d, we have reported our results of polarization-dependent InP-like optical modes in the backscattering geometry for #T1, T2, T4, and T5 samples. Here, the polarization of the incident beam is fixed while analyzing the scattered light by using a Polaroid sheet combined with a half-wave plate, placing it in front of the entrance slit of laser light. The RSS spectra are recorded with two different polarization geometries z(XX)\(\overline{\rm{z}}\) and z(YX)\(\overline{\rm{z}}\), where X, Y, and Z denote the coordinates of (100), (010), and (001) directions with respect to the n-InAs substrate. In the polarization-dependent RSS, the characteristic of \(\omega_{\rm{TO}}\) in the backscattering geometry is a non-polar optical mode that carries intrinsic crystalline information. No matter how we rotate the InP\({}_{1-x}\)Sb\({}_{x}\)/n-InAs sample with respect to the z-axis, the intensity of \(\omega_{\rm{TO}}\) phonon remains constant [Figure 3a-d].  
Like other Raman studies [24; 25; 26; 27], our composition-dependent results have indicated that the InP\({}_{1-x}\)Sb\({}_{x}\) ternary alloys exhibit a two-phonon-mode behavior (see Figure 4). We attribute this behavior to the strong bond bending in InPSb [40]. Again, in the limiting values of composition x for InP\({}_{1-x}\)Sb\({}_{x}\), we are unable to identify the impurity vibrational modes (i.e., the gap mode of InP:Sb near x \(\to\) 0 and LVM for In\(Sb\):P near x \(\to\) 1). While the LVM in P-doped InSb is observed by FTIR spectroscopy [43; 44], no such measurement of the gap mode has been reported for InP:Sb. Our ATM-GF calculation (Section 4.1.2), providing accurate values of the impurity modes, has offered further justification for the two-phonon-mode behavior [53; 54; 55; 56; 57; 58; 59].  
Figure 4: Comparison of the modified-random-element iso-displacement (MREI) model with our Raman scattering data for the two-phonon mode behavior of InP\({}_{1-x}\)Sb\({}_{x}\) alloys. The blue arrows represent our results of LVM and gap mode using ATM-GF theory (see text).
## 3 Results
### Registration Accuracy Analysis  
Optical scanning and X-ray CT differ in terms of the fine dimensions of 3D models owing to their different light sources and data acquisition methods [37; 38]. In this study, optical scanning showed a higher dimensional accuracy because it directly acquired geometric information via triangulation [39; 40]. Hence, to perform 3D convergence modeling, it is necessary to analyze the shape difference and registration accuracy of the two models in advance. In this study, the registration accuracy was examined by aligning the optical scanning and X-ray CT models, followed by the visualization of their RMS deviations.  
For this analysis, the X-ray CT model with a low point spacing was fixed as the reference model and the optical scanning model was set as the analysis model. Then, the 10 most prominent common features of the two models were selected, aligned, and registered using the ICP algorithm. From the registered 3D models, we found that the overall size of the optical scanning model was larger than that of the X-ray CT model; the X-CT model exhibited larger dimensions only in the upper part of the body. Upon analyzing the points where the two models showed large geometric differences between them, the average distance was found to be ~0.3 mm and the largest distance was found to be 0.5 mm (Figure 8).  
A deviation analysis was performed for the overall geometric difference between the optical scanning and X-ray CT mesh models to calculate the minimum, maximum, and average deviations as well as the RMS deviation (Figure 9). The results demonstrated that the two models deviated by \(\pm\)0.5 mm, and the average deviation was 0.07 mm in the (+) direction. The fact that the average deviation between the two models was along the (+) direction indicates that the optical scanning model (i.e., the analysis model) was geometrically larger than the X-ray CT model. In addition, the 3D model that mapped the RMS deviation effectively represented the geometric differences depending on the location of individual polygons. The average RMS deviation was found to be 0.13 mm.  
This deviation cannot be easily attributed to a single factor. X-ray CT can cause errors in the cross-sectional image depending on the direction of the light source and the scattering effect of the material during X-ray transmission. In addition, dimensional differences with respect to optical scanning results are inevitable because the surface is determined through cross-sectional images. Furthermore, the different curvatures and complex geometric information of the optical scanning and X-ray CT models affect the registration process. However, this RMS deviation is smaller than the point spacings of the optical scanning and X-ray CT models; hence, the deviations would not have a notable impact on the merging process. Therefore, mesh-based convergence modeling was subsequently performed.  
Figure 8: Distance differences observed after aligning optical scanning mesh and X-ray CT voxel models.