esa-sceva/satcom-chunk-collection huggingface dataset:
# Construction and Application of an Ecological Quality Evaluation System Based on a PIE-Engine +
## 3 Results and Discussion
### Spatiotemporal Variation in ESI in the Taihu Basin  
The spatial and temporal variations in the ESI in the Taihu Basin were also obvious, exhibiting an initial decrease followed by an increase. The average value of ESI was 0.687 in 2001, subsequently declining to 0.421 in 2010, marking a decrease of 0.266. By 2020, it had arisen to 0.731, indicating an increase of 0.310 compared to 2010. These trends suggested a decline followed by the gradual improvement in the ecological stability of the Taihu Basin from 2001 to 2020, ultimately reaching its highest value in 2020.  
Using the distribution in 2020 as an example (Figure 5), the ESI of forest and grassland in the southwestern region exhibited high values. Conversely, in the southeastern, southern, and western regions, dominated by croplands and wetlands, the ESI was relatively low. This indicated that different land use types are the primary factors affecting ecological stability. Consequently, based on the implementation of measures such as \"Returning Cropland to Forests\", \"Returning Cropland to Grassland\", and \"Returning Cropland to Lakes\", promoting integrated protection and restoration efforts for mountains, water body, forests, lakes, grassland, and sands from the perspective of ecosystem integrity, ecosystem stability can be enhanced.  
Figure 4: Distribution of changes in EFI. (**a**) 2001–2011. (**b**) 2011–2020.  
Figure 5: Distribution of ESI for the Taihu Basin in 2020.
## 5 Solving the Dilemmas
### Anticipatory Troubleshooting Dilemmas as a Markov Decision Process
#### 5.2.2 Replace-Healthy Dilemma as an MDP  
Next, we describe our approach to model the replace-healthy dilemma as a set of MDPs. For every healthy component \(C\), we define the following MDP designed to decide whether it is worthwhile to replace \(C\) or not at this stage.  
**States and actions.** A state in this MDP is defined exactly a state in \(MDP_{\text{FixOrReplace}}\). The set of actions in this MDP is also the same as in \(MDP_{\text{FixOrReplace}}\), except for the actions in the initial state. In this state, we allow one of the following two actions: _Replace_(\(C\)) and _Ignore_(\(C\)). _Ignore_(\(C\)) means we decide not to replace \(C\) at the current time step. Note that there is no _Fix_(\(C\)) option here since \(C\) is currently healthy.  
**Transition function.** As in \(MDP_{\text{FixOrReplace}}\), applying an action has two possible outcomes: either \(C\) is faulty in the next time interval or not, denoted \(s^{\prime}_{fail}\) and \(s^{\prime}_{ok}\), respectively. The values of \(S_{C}\) and \(C_{Age}\) after _Replace_(\(C\)) or _Fix_(\(C\)) are exactly the same as in \(MDP_{\text{FixOrReplace}}\). After performing _Ignore_(\(C\)), the corresponding survival curve does not change and the age increases with time. The row for _Ignore_(\(C\)) in Table 2 describes these changes in \(S_{C}\) and \(C_{Age}\) in a formal way. The transition function \(\mathit{Tr}(s,a,s^{\prime})\) is exactly the same as in \(MDP_{\text{FixOrReplace}}\):  
\[\mathit{Tr}(s,a,s^{\prime})=\begin{cases}S^{\prime}_{C}(C^{\prime}_{Age}- \Delta_{T})-S^{\prime}_{C}(C^{\prime}_{Age})&s^{\prime}=s^{\prime}_{fail}\\ 1-\left(S^{\prime}_{C}(C^{\prime}_{Age}-\Delta_{T})-S^{\prime}_{C}(C^{\prime} _{Age})\right)&s^{\prime}=s^{\prime}_{ok}\\ 0&\text{Otherwise}\end{cases} \tag{6}\]  
**Reward function.** The reward function \(R(s,a)\) of this MDP is the negation of the cost of the executed action. For _Replace_(\(C\)), this cost is \(cost(\textit{Replace}(C))\). Note that we do not add here the overhead cost, since the replace-healthy dilemma arises when already in a troubleshooting process. Obviously, the cost of _Ignore_(\(C\)) is zero.
## 2 Study Area and Data Preparation
### Data Preparation
#### 2.2.2 Impact Factor
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Impact Factor** & **Type of Data** & **Class** & **Number Classified** & **Information Value** \\ \hline \multirow{8}{*}{Aspect} & Flat & 21,604 & 0 \\  & & North & 1,125,411 & \(-\)1.15356 \\  & & East north & 2,261,127 & \(-\)0.22297 \\  & & East & 2,562,512 & 0.71887 \\  & & East south & 2,576,035 & \(-\)0.05637 \\  & & South & 2,190,668 & \(-\)0.15461 \\  & & West south & 2,254,787 & 0.38001 \\  & & West & 2,526,164 & \(-\)0.45276 \\  & & West north & 2,556,362 & \(-\)0.30899 \\  & & North & 1,104,315 & \(-\)0.54158 \\  & & (\(>\)337\({}^{\circ}\)) & 1,104,315 & \(-\)0.54158 \\ \hline \multirow{8}{*}{Lithology} & \multirow{8}{*}{Discrete Data} & Metamorphic & \multirow{2}{*}{271,429} & \multirow{2}{*}{\(-\)0.57646} \\  & & rock & & \\  & & Pyroclastic & & \\  & & rock & 151,520 & 0.11188 \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\ \hline \multirow{8}{*}{Land use} & \multirow{8}{*}{Discrete Data} & Plough & 2,684,675 & 0.85814 \\  & & Forest & 15,534,117 & \(-\)0.29388 \\  & & Grass land & 475,696 & 0.63739 \\  & & Shrub land & 32,132 & 0 \\  & & Wet land & 6 & 0 \\  & & Water & 209,280 & \(-\)1.12550 \\  & & Artificial & & \\  & & surface & 299,034 & 0.17585 \\  & & & Bare & 409 & 0 \\  & & ground & & \\ \hline \multirow{8}{*}{Distance to} & \multirow{8}{*}{Discrete Data} & 0\(\sim\)150 & 2,012,221 & 0.17929 \\  & & 150\(\sim\)300 & 1,936,147 & \(-\)0.01950 \\  & & 300\(\sim\)450 & 1,814,044 & 0.12293 \\  & & \(>\)450 & 13,445,483 & \(-\)0.04526 \\ \hline \multirow{8}{*}{Distance to} & \multirow{8}{*}{Discrete Data} & 0\(\sim\)150 & 823,194 & 0.80861 \\  & & 150\(\sim\)300 & 759,897 & 0.08909 \\  & & 300\(\sim\)450 & 718,022 & 0.63598 \\  & & \(>\)450 & 16,906,782 & \(-\)0.10819 \\ \hline \multirow{8}{*}{Distance to} & \multirow{8}{*}{Discrete Data} & 0\(\sim\)150 & 2,068,488 & 0.79800 \\  & & 150\(\sim\)300 & 1,529,259 & 0.81112 \\ \cline{1-1}  & & 300\(\sim\)450 & 1,303,437 & 0.36722 \\ \cline{1-1}  & & \(>\)450 & 14,306,711 & \(-\)0.43176 \\ \hline \multirow{8}{*}{Annual} & \multirow{8}{*}{Discrete Data} & \(<\)1396 & 1,225,458 & \(-\)0.23757 \\ \cline{1-1}  & & 1396\(\sim\)1480 & 1,983,300 & \(-\)0.44458 \\ \cline{1-1} \cline{2-4}  & & 1480\(\sim\)1548 & 3,097,072 & 0.10051 \\ \cline{1-1} \cline{2-4}  & & 1548\(\sim\)1617 & 4,859,072 & \(-\)0.66094 \\ \cline{1-1} \cline{2-4}  & & 1617\(\sim\)1686 & 3,275,853 & 0.12061 \\ \cline{1-1} \cline{2-4}  & & 1686\(\sim\)1764 & 2,801,493 & 0.49835 \\ \cline{1-1} \cline{2-4}  & & 1764\(\sim\)1863 & 1,296,538 & \(-\)0.14734 \\ \cline{1-1} \cline{2-4}  & & \(>\)1863 & 669,130 & 0.87835 \\ \hline \hline \end{tabular}
* NDVI—normalized difference vegetation index. TWI—topographic wetness index.  
\end{table} 
Table 2: _Cont._
# Solution for Interference in Hotspot Scenarios Applying Q-Learning on FFR-Based ICIC Techniques
## 8 Preliminary Analysis B: Factorial Design using the Strict Frequency Reuse Algorithm
### Evaluation Scenario  
The proposed scenario is very similar to the one described in Section 7.1, as illustrated in Figure 11. It consists of three eNBs positioned in the vertices of an equilateral triangle with side equal to 1000 m. A total of 80 users are randomly positioned in the entire scenario according to a uniform distribution. They do not have mobility and each UE is served by the closest eNB. The simulation parameters are presented in Table 3.  
Figure 10: Scenario 2 from preliminary analysis A: SINR CDF for less concentrated users (R = 500 m). Each curve represents a different ICIC algorithm.
# Unsupervised Image Classification Based on Fully Fuzzy Voronoi Tessellation
## 2 Related Work
### Voronoi Tessellation and Hidden Markov Random Field FCM (VT-HMRF-FCM)  
VT-HMRF-FCM introduces the implicit role of Voronoi tessellation in the objective function; it is,  
\[J_{\text{VT-HMRF-FCM}}(\mathbf{U},\mathbf{\theta})=\sum\limits_{j=1}^{m}\sum\limits_{l =1}^{k}u_{jl}D_{jl}+\eta\sum\limits_{j=1}^{m}\sum\limits_{l=1}^{k}N_{j}u_{jl} \log\frac{u_{il}}{\pi_{jl}} \tag{18}\]  
where \(\mathbf{U}=\{u_{jl},j=1,\ldots,m,l=1,\ldots,k\}\) is the polygon level fuzzy membership about Voronoi polygon \(j\) and cluster \(l\), and \(u_{jl}\) needs to be satisfied in the condition \(\sum\limits_{l=1}^{k}u_{jl}=1,j=1,\ldots,m\). \(D_{jl}=\sum\limits_{i\in\mathbf{P}_{j}}d_{il}\) represents the polygon level distance between polygon \(j\) and cluster \(l\). \(\mathbf{P}_{j}\) represents \(j\)th Voronoi polygons. \(d_{il}\) is the distance between pixel \(i\) and cluster \(l\). It is defined as the negative logarithm of the probability density function of Gaussian distribution. \(\mathbf{\theta}=\{\mathbf{\theta}_{l},l=1,\ldots,k\}\) is the clustering center parameter, described in this algorithm as the mean and variance of a Gaussian distribution function. \(\pi_{jl}\) is the prior probability between polygon \(j\) and cluster \(l\). It is defined according to Markov random field. \(N_{j}\) is the number of pixels in polygon \(j\). \(\eta\) is the regularization coefficient.  
\[d_{il}=-\log p(\mathbf{X}_{i}|\mathbf{\theta}_{l}) \tag{19}\]  
\[\pi_{jl}=\frac{\exp\left[\beta\sum\limits_{j^{\prime}\in\mathcal{S}_{j}}\xi(L_{ j},L_{j^{\prime}})\right]}{\sum\limits_{l^{\prime}=1}^{k}\exp\left[\beta\sum \limits_{j^{\prime}\in\mathcal{S}_{j}}\xi(L_{j}=l^{\prime},L_{j^{\prime}}) \right]} \tag{20}\]  
where \(\xi\) (\(A,B\)) is a binary function; if \(A=B\), \(\xi\) (\(A,B\)) = 1, otherwise \(\xi\) (\(A,B\)) = 0. \(\beta\) indicates the strength of the neighborhood effect. \(L_{j}\) represents the cluster label of polygon \(j\) belongs to.
## 1 Introduction
Extensive simulations have been performed to compare the proposed model against state-of-the-art methodologies using the MobFogSim simulator [10]. The following are the highlights of the contributions made in this work. Figure 1: Scenarios of task offloading with user mobility and handovers. * In MEC, we propose a novel task offloading framework called COME-UP, which is composed of two sub-models. Time series analysis combined with LSTM is used by the location and direction prediction model;
* The task placement model makes use of a fitness function based on the weighted sum method to determine which edge server is the most suited;
* We formulate a fitness function that determines the optimized server using different priority weights for latency (\(\alpha\) ), energy (\(\beta\)), and server load (\(0\));
* The performance comparisons utilizing MobFogSim show that the COME-UP outperforms the others (DMPO and TAOM) with a 32% reduction in latency, 16% energy savings, and increasing resource (CPU) utilization by 9%. The root mean square error of the LSTM model is noted as 0.5. The rest of the paper is structured in the following manner. Section 2 discusses the findings of a literature review on mobility models and mobility-aware offloading approaches. Section 3 demonstrates the limitations of mobility prediction using a real-world use case. Section 4 describes the system overview and the models that are used, followed by the proposed framework. Section 5 discusses the experimental assessments and comparisons that were conducted. Section 6 brings the work to a conclusion, and Section 7 discusses some potential research directions for future work.
# Federated Learning-Based Security Attack Detection for Multi-Controller Software-Defined Networks
## 2 Related Works
Conversely, as presented in [21], the spatial aspects of the SDN traffic flow have been investigated using the enhanced CNN model with graph features, namely Spatial-Temporal Graph Convolutional Network (ST-GCN). With the assistance of In-band Network Telemetry support, the ST-GCN model identifies the SDN switches impacted by DDoS attacks. The work in [22] presents the application of a Perceptron-based deep learning technique to improve Quality of Service (QoS) and security within software-defined networks. The limitations in this work dataset cannot reflect the characteristics of the SDN environment. In order to enhance security and handle distributed network traffic, several security researchers have employed federated learning models against emerging attack types. For example, Deep Monitor [23] is an intelligent flow rule match-field control system that monitors fine-grained network traffic in SDN-based IoT edge nodes considering maximum flow table capacity. Moreover, to improve the learning ability of the edge nodes, the work in [23] applied a federated Double Deep Q network as the traffic monitoring mechanism. Another research work in [24] applied the Weighted Federated Learning to identify the Low-Rate DDoS attacks. By examining the model accuracy, a robust preference assignment mechanism in [24] leverages the federated server to prioritize the locally trained models. Table 1 comparatively reviews several learning-based SDN security research works.
# Real-Time Station Grouping under Dynamic Traffic for IEEE 802.11 ah
## 5 Optimal Input Parameter Derivation  
In addition to information about past transmissions, the TAROA algorithm takes two additional input parameters: (i) the optimal number of stations in one RAW slot \(\sigma^{r}_{opt}\); and (ii) the maximum number of packet transmissions in one beacon interval \(\pi^{b}_{max}\). The optimal value for each of these parameters can be derived analytically or experimentally for a given network topology and configuration. This section describes how to derive these values.
## Materials and Methods  
### Materials and Processing  
#### 2.1.1 Nanofiller and Polymer Matrix  
Commercial fumed silica and polylactide were respectively selected as nanofiller and polymer matrix. In particular, Aerosil(r)r805 silica nanoparticles, a hydrophobic-fumed silica type (density of 1.99 g\(\cdot\)cm\({}^{-3}\) and a BET surface area of 150 \(\pm\) 25 m\({}^{2}\cdot\)g\({}^{-1}\)), obtained by treating natural fumed silica nanoparticles with octylsilane (-C\({}_{8}\)H\({}_{17}\)), was provided by Degussa (Hanau, Germany). Polylactide (PLA) granules were supplied by NaturePlast (Caen, France) under the trade name of PLE 005 (enantiomeric purity of L-lactide of about 94 wt% and D-lactide of about 6 wt%), MFI = 16 g\(\cdot\)10 min\({}^{-1}\) at 190 \({}^{\circ}\)C and 2.16 kg, intrinsic viscosity at 25 \({}^{\circ}\)C = 1.35 g\(\cdot\)dl\({}^{-1}\), density = 1.25 g\(\cdot\)cm\({}^{-3}\). Both fumed silica and PLA chips were utilized as received.
# Hidden Charge Orders in Low-Dimensional Mott Insulators
## 2 Non-Local Orders in Interacting Low Dimensional Insulators
### One Dimensional Topological Mott Insulators  
In particular, in the case of the HI phase, one can prove that at the edge sites a fractional charge always accumulates [18], differing from the bulk one. In fact, while the average on-site charge is one, two degenerate states with non-integer charges \(e^{(\pm)}(x_{\chi})\) occur at the left (\(\chi=-\)) and right (\(\chi=+\)) edges. At half-filling, within the bosonization approach, such charges can be evaluated as the average charge plus the charge kink at the edge. Explicitly,  
\[e^{(\pm)}(x_{\chi})=1+2\lim_{a\to 0^{+}}\frac{1}{\sqrt{2\pi}}[\phi_{c}(x_{ \chi})-\phi_{c}(x_{\chi}-a)]=1\pm\frac{\chi}{2}\quad, \tag{7}\]  
where the sign \(\pm\) descends from the pinning value of the charge field, also reported in first column of Table 1. More precisely, one of the two degenerate states has a left-edge charge equal to \(3/2\) and a right-edge charge equal to \(1/2\), whereas the other has the opposite arrangement. This is qualitatively reproduced in the cartoon shown in Figure 1.  
The two states with non-trivial topology cannot be deformed adiabatically one into the other without closing the gap, nor into the trivial Mott insulator state, characterized by the non-vanishing of the charge parity. Thus, the phases are distinct. This is true at least in the presence of particle-hole (\(P\)) symmetry, which action on the bosonic fields \(\phi_{v}(x)\), \(\theta_{v}(x)\) can be expressed as  
\[P\phi_{v}P^{-1}=-\phi_{v}\,\ P\theta_{v}P^{-1}=-\theta_{v}\quad. \tag{8}\]  
From the above relations, one verifies that P is a symmetry of both \(H_{v}\)s. Moreover, to change adiabatically the pinning values of the field \(\phi_{v}\) one into the other, one must break such symmetry. Hence, \(P\) configures as the symmetry protecting the topological nature of the phase.  
We emphasize that both the phases classification reported in Table 1 and the above topological properties (7) of the ground state of the Hamiltonian are obtained from the RG analysis of its asymptotic behavior and its symmetries. However, the topological aspects can also be understood [18] within the more general framework of symmetry protected topological phases [19; 20; 21; 22]. In the last column of Table 1, for each of the insulating phases, it is shown which are the internal symmetries protecting the non-trivial phases, whereas triv indicates the phases with trivial topological properties. Moreover, \(T\) denotes the time reversal symmetry, which action reads:  
\[T\phi_{v}T^{-1}=\delta_{v}\phi_{v}\,\ T\theta_{v}T^{-1}=-\delta_{v}\theta_{v} \tag{9}\]  
with \(\delta_{c}=1\), \(\delta_{s}=-1\).
# Assessment of a Patient Dose Monitoring System for Average
## 2 Materials and Methods
Since DoseWatch does not store acquired mammograms, the images to be analyzed with LIBRA software were downloaded patient by patient from the Picture Archiving and Communication System (PACS) located at Humanitas ICC. For this reason, two subgroups were extracted from the total patient population (119 patients for Dimensions 1 and 124 patients for Dimensions 2), resulting in a final dataset of 1051 mammograms (243 women), all of which fell within the age, HVL, and thickness ranges tabulated by [PERSON]'s model. The AGD was calculated for these subgroups of patients using the methods described by [PERSON] and [PERSON] and Equations (1) and (2), respectively, with the air kerma calculated value and the effective glandularity estimated by LIBRA. As before, the results of these calculations were compared with the AGD values provided by the mammography units and extracted from DoseWatch. To evaluate the statistical significance of the differences found in the AGD calculations, a two-tailed paired _t_-test was used, with the significance level set at 0.05. Table 1 shows some DoseWatch data relative to all the images included in the study, divided by mammographic unit, as well as the two subgroups of patients selected for glandularity estimation using LIBRA. For comparison, the ranges of age, HVL, and CBT values tabulated according to the [PERSON] and [PERSON] models and used in the AGD calculation are also shown.
## 2 ITS Architecture and Applications
### ITS Applications
#### 2.3.4 Autonomous Driving Applications  
Autonomous driving, also known as automated driving, applications represent the next big leap in human transport technologies, which is expected to be deployed by 2020 and fully functional by 2030 [31].  
This new technology will rely on the automation of the vehicle sensing and driving functions, based on six levels of automation [31], where the human driver becomes a passenger and is no longer required (_i.e._, full automation level). As shown in Figure 6, future autonomous cars will integrate different technologies, including: (i) ultrasonic sensors to detect the presence of obstacles; (ii) LiDAR and/or radar to create a 360-degree field of view to prevent accidents; (iii) high definition cameras to spot road hazards in real time, such as pedestrians and animals; (iv) Global Navigation Satellite System receivers to provide a highly accurate position for the car; and (v) V2X communication technologies to enable the car to communicate with the surrounding vehicles, road infrastructures, remote services providers and trusted third parties. In addition to the previously described applications, autonomous driving technology will bring a wide range of new benefits, in terms of the increase of the roadway and parking capacity and the reduction of traffic congestion, car theft, accidents and collisions.  
In order to unlock the tremendous potential of the aforementioned emerging ITS applications, the ITS communication stack should provide efficient, secure and low-latency V2X communications. Indeed, safety applications require the periodic broadcasting of safety messages (or beacons) to detect and/or prevent the risk of collision between two or more vehicles or to locate hazards along the road. However, since this message exchange relies heavily on wireless communications, several threats and attacks can affect its functioning and, thus, lead to accidents. In the next sections, we will review the key enabling ITS standards, technologies and projects, followed by a detailed analysis on the existing ITS threats and their main cryptographic countermeasures.
# An Uncertainty-Driven Proactive Self-Healing Model for
## 3 Results
### Performance Assessment
Concerning the number of tasks with high priority or high demand, \(\eta\) and \(\omega\) depict that they are affected in the opposite direction to \(\phi\), as we notice a slight decrement in their values. When we focus on the number of times that the local queue is full, we perceive that it is increased when we increase \(\beta\), as the \(\psi\) metric shows. Figure 6: Time comparison of results for BM, UPSHM, DM. Figure 7: Comparison of UPSHM performances for all metrics for different values of \(\beta\) threshold. In Figure 8, we present the time performance of UPSHM for \(\theta=85\%\) and \(\beta=\{0.8,0.84,0.88\}\). As we can see, \(AvT\) decreases as the value of \(\beta\) increases. \(AvT\) in the worst case is equal to 0.7 seconds and in the best case it is approximately equal to 0.53 seconds. In Figure 9, a comparison of the performance of the UPSHM for different values of \(\theta\) and \(\beta=0.8\) is presented. As previously, the values of \(\phi\) and \(\psi\) metrics increase as \(\theta\) increases. In contrast, the values of metrics \(\eta\) and \(\omega\) are affected in an adverse direction. Figure 8: \(AvT\) of UPSHM for different values of \(\beta\). Figure 9: Comparison of UPSHM performances for all metrics for different values of the \(\theta\) threshold. In Figure 10, when we focus on the effect of \(\theta\) on the time performance of the UPSHM, we can see that \(AvT\) decreases as \(\theta\) increases. As previously, in the worst case, \(AvT\) is equal to 0.7 s, while in the best case the average time for decision offloading is slightly higher than 0.6 s.  
Observing Figures 7 and 9, we can draw conclusions concerning the effect of \(\theta\) and \(\beta\) in the performance of the UPSHM. We see that \(\phi\) has almost the same performance in both figures. When we focus on \(\eta\), \(\omega\) and \(\psi\), we notice that both thresholds affect the performance of the UPSHM in a negative manner. However, an increment in \(\beta\) has greater negative effects than the increment of \(\theta\). Additionally, from Figures 8 and 10 we notice that the less time UPSHM spends to make the decision, the greater the values of the \(\psi\) and \(\phi\) metric become. Simultaneously, the values of \(\eta\) and \(\omega\) increase.