esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Disease Spreading Behavior Oriented to Three-Layer Complex Networks
## 2 Introduction to Basic Concepts
### Introduction to Immunization Strategy  
The impact of real-life spreading phenomena is divided into a positive and negative element. For the spread of viruses, infectious diseases, and negative spread of social opinions and rumors, immunization strategies should be implemented in time to reduce the negative impact of spread. Strategies such as random immunization, targeted immunization, and acquaintance immunization, are often used to suppress the spreading procedure.  
Random immunization is the random selection approach of some nodes to be immunized with equal probability. Targeted immunization is the selective method of a small number of core nodes on the network for immunization. The selected probability is various, which depends on the position of the nodes on the networks, and it is likely to be more effective if the more important nodes are found for immunization. The acquaintance immunization strategy first performs random immunization and then it randomly selects neighboring nodes from the selected nodes again for immunization. According to the immunization algorithm, acquaintance immunization can achieve better immunization performance without knowing the global node information of the networks.  
The most-used immunization algorithms are target immunization and acquaintance immunization, and improved algorithms based on these immunization algorithms.  
[PERSON] et al. [20] proposed a rumor immunization strategy based on a multi-community system and demonstrated that the strategy is more effective in inhibiting the further spread of rumors in the middle and later stages of the spread. [PERSON] et al. [21] proposed an innovative acquaintance immunization strategy, named after AI-CWD, which considers influencing factors, such as edge weight and degree value, respectively. [PERSON] et al. [22] proposed a locally optimal virus immunization strategy based on a weighted scale-free network and verified the effectiveness of such immunization strategy. [PERSON] et al. [23] proposed a ternary group-edge immunization strategy and compared it with other immunization strategies. The comparison results demonstrate that the ternary group-edge immunization is more effective when the infection rate is larger, and its effectiveness is inferior to that of the edge immunization of degree-degree. The proposal of immunization strategy is an important way to inhibit the spread of infectious diseases, rumors, viruses etc. It is still a topic whether the immunization strategy can meet the requirements of real networks and it can be applied on real networks.
Superactivating Bound Entanglement in Quantum Networks via Quantum Zeno Dynamics and a Novel Algorithm for Optimized Zeno Evolution  
[PERSON]  
1 Institute for International Strategy, Tokyo International University, 1-13-1 Matoba-kita, Kawagoe 350-1197, Saitama, Japan 12 CERN, CH-1211 Geneva, Switzerland 2*12  
[PERSON]  
3 Faculty of Engineering and Natural Sciences, Isik University, Sile, 34980 Istanbul, Turkey 3  
[PERSON]  
4 Department of Physics, Faculty of Science, Istanbul University, Vezneciler, 34116 Istanbul, Turkey 4  
[PERSON]  
5 Engineering Faculty, Istanbul Technical University, Maslak, 34469 Istanbul, Turkey 56 Faculty of Engineering, Bogazici University, Bebek, 34342 Istanbul, Turkey 6  
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
Footnote 2: [[https://www.mdpi.com/journals/article/pdf/10](https://www.mdpi.com/journals/article/pdf/10)]([https://www.mdpi.com/journals/article/pdf/10](https://www.mdpi.com/journals/article/pdf/10))  
between distant parties [10], and quantum metrology [11]. The other way is to activate [12] or superactivate [13] BE. In [12], [PERSON] et al. considered two spatially separated parties sharing a bipartite mixed FE system and many copies of a bipartite BE system, and all the particles are qutrits. In an iterative fashion, each party applies a two-qutrit operation on the particle of the FE state and the particle of one of the BE states. Then they measure the latter particle on a \(z\)-basis and communicate the results. If the results match, the entanglement of the FE state is increased in each iteration. Otherwise, it is not decreased. In the next iteration, a new BE state is used.  
As will be detailed below, [PERSON] et al. considered two four-qubit BE states shared among five nodes of a quantum network [13]. By teleporting the unknown state of particles, nodes can obtain a two-qubit distillable FE state, which is called the superactivation of BE.  
In this work, we seek the possibility of superactivating bound entanglement via quantum Zeno dynamics (QZD). Beyond a practical point of view, by removing the controlled gates, our work contributes to the fundamentals of quantum entanglement and measurements. What is more, following the distillation of two-qubit entanglement as in the original superactivation work [13], our first major contribution is to show how all the qubits in the quantum network among five nodes can be made free entangled by implementing a simple QZD procedure. Our second major contribution is the design of a general algorithm for optimizing the QZD evolution.  
This paper is organized as follows. First, we introduce the superactivation of BE by [PERSON] et al. [13] and review the basics of QZD. In Section 2, we present our QZD procedure and how to confirm the superactivation, and we present our optimization algorithm. In Section 3, we present two scenarios for the superactivation of BE and apply the algorithm to the second scenario. We discuss various aspects of our scenarios, including the drawbacks and future works.
## 4 Analysis Results
### Monte Carlo Simulation of Flexural Behavior  
The previous section has shown that the proposed method, which considered inhomogeneous material properties of each segment, accurately described the flexural behavior of HPFRCC beams including flexural ductility as well as crack width. With the proposed method, it should be noted that the analysis results can be variable, since different material properties are assigned for each analysis. In this study, therefore, the Monte Carlo simulation [35], which is widely used in an analysis with random variables, has been implemented in the proposed method in order to consider the uncertainty for the material properties of HPFRCC. The simulation analysis was conducted 1000 times, and results with 99% certainty were extracted.  
Normal distributions were assumed for the test parameters, since no sufficient data have been accumulated so far for this research area. The first cracking strain and strength, and the ultimate strain and strength were randomly assigned according to the probability distributions assumed. Measured results are used for the mean values of the normal distributions. However, the coefficients of variation were adapted from the [PERSON]'s round-robin test [36] since the number of measurements was limited. The coefficients of variation (CoV) for the first cracking strain and strength were 16.5% and 22.5%, respectively. The CoVs for the ultimate cracking strain and strength were 24.3% and 9.4%.  
The result obtained from the simulation is compared with the test results in Table 2 and Figure 13. The maximum moment was 1661 kN\(\cdot\)mm with a standard deviation 46.06. The average deflection at the maximum moment was 1.284 mm and the standard deviation was 0.164. The measured deflections are within an interval of 99% confidence, between 1.014 and 1.554 mm.  
Figure 12: CMOD–deflection curve.  
For maximum crack widths, the measurements were greater than the predictions. This result may have been caused by the limitations of the prediction model and measuring method. The crack widths were measured using a CMOD gauge, which measures the change of length between two steel tips as shown in Figure 5, so that it includes deformations caused by the section other than the targeting crack width itself. For HPFRCC, [PERSON] _et al._[17, 18] have reported that many invisible cracks form in the matrix. Therefore, the measured crack width tends to be overestimated. Meanwhile, since the elastic part of the material model adapted in this study is assumed to be elastic after the crack forms, predictions tend to underestimate the crack width.
## 3 Results and Discussion
### Air Tightness  
Figure 11 presents the results of the air tightness tests and indicates that the chamber has leakages of air. In these air tightness tests, the pump was turned on to pressurize/depressurize the chamber and then turned off about 100 s from the start of the test. After turning off the pump, the barometric pressure returned to its initial values after about 100 and 200 s in the positive and negative pressure tests, respectively. Short- and long-term tests demonstrated that the control system is able to reach and maintain the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Test**} & **Setpoint** & \multirow{2}{*}{**Min (hPa)**} & **Average** & \multirow{2}{*}{**Max (hPa)**} & **Standard** \\  & **(hPa)** & & & & **(hPa)** \\ \hline Test 1—stage 2 & 945 & 944.97 & 945.00 & 945.07 & 0.024 \\ Test 1—stage 4 & 955 & 954.91 & 955.00 & 955.07 & 0.034 \\ Test 2—stage 2 & 935 & 934.90 & 934.99 & 935.08 & 0.036 \\ Test 2—stage 4 & 925 & 924.90 & 925.00 & 925.12 & 0.051 \\ Test 3—stage 2 & 945 & 944.98 & 945.00 & 945.04 & 0.024 \\ Test 3—stage 4 & 955 & 954.97 & 955.00 & 955.08 & 0.027 \\ Test 4—stage 2 & 935 & 934.97 & 935.00 & 935.03 & 0.022 \\ Test 4—stage 4 & 925 & 924.96 & 925.00 & 925.02 & 0.026 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Stability of barometric pressure (hPa) during the stages of steady setpoint (stages 2 and 4). The table shows descriptive statistics of the pressure setpoint for both short- and long-term tests.  
setpoint pressure although air leakage exists. In addition, perhaps a small level of leakage might be beneficial to enable air renewal within the chamber during the experiments. The system could be improved by installing sensors to monitor gas concentrations (e.g., O\({}_{2}\), CO\({}_{2}\)) inside the chamber.
# Using BiLSTM Networks for Context-Aware Deep Sensitivity Labelling on Conversational Data
###### Abstract  
Information privacy is a critical design feature for any exchange system, with privacy-preserving applications requiring, most of the time, the identification and labelling of sensitive information. However, privacy and the concept of \"sensitive information\" are extremely elusive terms, as they are heavily dependent upon the context they are conveyed in. To accommodate such specificity, we first introduce a taxonomy of four context classes to categorise relationships of terms with their textual surroundings by meaning, interaction, precedence, and preference. We then propose a predictive context-aware model based on a Bidirectional Long Short Term Memory network with Conditional Random Fields (BiLSTM + CRF) to identify and label sensitive information in conversational data (multi-class sensitivity labelling). We train our model on a synthetic annotated dataset of real-world conversational data categorised in 13 sensitivity classes that we derive from the P3P standard. We parameterise and run a series of experiments featuring word and character embeddings and introduce a set of auxiliary features to improve model performance. Our results demonstrate that the BiLSTM + CRF model architecture with BERT embeddings and WordShape features is the most effective (F1 score 96.73%). Evaluation of the model is conducted under both temporal and semantic contexts, achieving a 76.33% F1 score on unseen data and outperforms Google's Data Loss Prevention (DLP) system on sensitivity labelling tasks.  
BiLSTM; BERT; NLP; context-aware +
Footnote †: journal: Applied Sciences
## 5 Simulations Results and Discussions
### Case Study 1: Home Is Not Equipped with DGs
#### 5.1.4 User Comfort  
The user comfort is the delay faced by the appliance considered without DGs is depicted in Figure 8. The user comfort of our proposed GWDO-based ECS is set side by side with the existing algorithms, which are PSO, GA, ACO, WDO, BFA, GPSO, GBFA, and GACO. The overall delays in the operation of each appliance are shown in Figure 8, where the water heater faced the highest delay by using ACO, whereas the GBFA and BFA-based ECS gave zero delays. GWDO-based ECS delayed 0.4 h of the appliance's operation for the user. Additionally, in the case of the dishwasher, the GWDO-based ECS gave a 0.5-h delay, which is the lowest after the GBFA-based ECS. The overall average delay faced by the dishwasher for all algorithms is 0.63 h. In the case of the clothes dryer, our proposed GWDO-based ECS gave the lowest delay, which is 0.38 h, and the highest delay faced by the clothes dryer is in the case of using the GA-based ECS. In the case of the iron, the lowest delay faced is by using the BFA-based ECS, and the highest delay is recorded by using ACO, whereas the GWDO-based ECS gave a delay of 0.64 h. Additionally, the GBFA-based strategy gave the lowest delay time, and the GA-, PSO-, and ACO-based ECSs gave the highest delays. In the case of EV, the overall delay faced by EV in the case of all techniques is 1.15 h, where PSO and ACO gave the highest, and our proposed GWDO gave the lowest delay among all techniques.  
Figure 7: Case study 1: Carbon emission reduction.
## 2 Literature Review
Moreover, in underwater environments, electromagnetic signals easily dissipate, and thus, radars are not suitable in underwater environments. In 3D underwater environments, a UUV needs to be equipped with expensive 3D sonar sensors with sensing arrays, in order to measure the relative position of its neighbor UUV. However, the intensity sampling sensors used in our study are not sufficient for measuring the relative position of a neighbor UUV. In other words, intensity sensors used in our study cannot be used for measuring the relative position of a neighbor UUV. To the best of our knowledge, our study is novel in developing provably complete 3D distributed rendezvous controls, considering a UUV which can only measure the strength of signals emitted from neighboring UUVs. Moreover, our study is unique in introducing a 3D rendezvous control, which is robust to intensity sensor failures or UUV failures.
# Energy Efficiency in Short and Wide-Area IoT Technologies--A Survey
## 3 Energy Efficiency and Consumption Metrics and Models
### Energy and Power Consumption Models
#### 3.2.2 LPWANs  
A study on the energy consumption of LPWANs is presented in [81], using the first order radio energy dissipation model introduced in [82], and also applied later in [83] and [84]. The battery life of transceivers is estimated using the E91-AA alkaline battery model. It is stated that most of the device energy is consumed to transmit and receive \(k\)-bit messages over a distance \(d\), but also for signal processing operations, including (de)modulation, (de)spreading, and (de)coding. Moreover, the power amplifier is another element dissipating significant energy amounts, depending on environmental factors such as humidity, fog, rain, and transmitter/receiver distance. The following equation is used to evaluate the energy consumed by the transmitter:  
\[E^{Tx}(k,d)=E^{Tx}_{elec}(k)+E^{Tx}_{amp}(k,d), \tag{6}\]  
where \(E^{Tx}_{elec}\) and \(E^{Tx}_{amp}\) denote the radio circuitry and amplifier consumption, respectively. \(E^{Tx}_{elec}\) is dependent on the techniques used to process the signal, such as modulation, spreading and coding; while \(E^{Tx}_{amp}\) is dependent on the environmental factors, one of which is the distance between transmitter and receiver.  
At the receiver, the consumption is due to the radio operations for detecting the signal. Hence, it can be expressed as a function of the number of \(k\)-bit messages and the consumption of the radio circuitry \(E_{dec}\) :  
\[E^{Rx}(k,d)=E^{Rx}_{elec}(k)=k\cdot E_{elec}. \tag{7}\]  
Different technologies, such as LoRa, LTE-M, NB-IoT, and Sigfox were tested in order to compare energy consumption and battery lifetime. The results showed that LTE-M has the highest energy consumption. In the case of distances less that 2 km, the energy consumption of the technologies is approximately the same, while at distances \(d\geq 2\) km, the energy consumption has significant gaps between each technology, with LTE-M and LoRa resulting as the highest and lowest energy-consuming technologies, respectively.
# An Overview of Shared Mobility Operational Models in Europe
## 4 Bikesharing
### Bikesharing in Europe  
According to [68], there were 813 bikesharing systems (BSS) at the time the article was published. The BSS in Paris, known as \"Velib\", is currently the largest in Europe, with 19,000 bikes and 1464 stations [69; 70]. Other BSS programs have also grown substantially, such as London with 12,000 bikes, Barcelona with 6000, and Lyon with 4000. Cities like Valencia, Seville, Milan, and Brussels each have over 2000 bikes. In countries like Spain, the number of BSS installations has surged, nearly doubling from 58 to 97 systems between 2008 and 2009. European cities are rapidly expanding their cycling infrastructure, with 32 cities building additional cycling infrastructure, with an example of Londonannouncing 100 km of new cycling infrastructure and Paris announcing 80 km of new cycling infrastructure [71; 72].  
Next bike is available in almost every main city in Europe and is expanding to cover all of Europe. Next bike can be station-based, free-floating, or even a hybrid system. Velib is specifically station-based, but the sheer number of stations that are available is very close to being free-floating from the customer's point of view. It is the largest in Europe, with over 19,000 bikes and 1464 stations [69]. Call a bike is also only station-based. It operates only in Germany but is available in 80 cities, including Berlin, Munich, Hamburg, Luneburg, Cologne, Frankfurt, Darmstadt, Stuttgart, and its region, among others. It operates on over 14,500 regular and electric bikes [73]. Bicimad is another BSS operating in Madrid, Spain. According to [74], they have over 140,000 users, 7735 bicycles, and 630 stations located in 21 districts. Santander Cycles is a BSS located in the United Kingdom in three cities: London, Brunel, and Milton Keynes. All the cities work on the same business model, station-based, and according to [75], it currently has 793 stations and 10,336 bicycles.  
As bikesharing systems continue to evolve through different generations and operational models, a similar development trend can be observed in the field of scooter sharing. This relatively new shared mobility modality is becoming increasingly popular due to changes in user behavior and advancements in technology. The main elements of scooter sharing, which include its operational models and European legal framework, will be examined in the following section.
## 4 Simulation and Experiment Validations
The machining process consists of three steps: rough machining, semi-finishing and finishing. The CNC system reads the trajectories using the proposed curve-fitting and feedrate scheduling method and then the axial commands are generated and sent to the controller. The machining results are shown in Figure 10.1  
The machining accuracy of the S-shape workpiece is affected by many factors, such as theoretical error, geometric error of the machine tool, and quality of toolpath [36]. Also, the measurement of freeform workpiece is very complicated. In this experiment, quality of toolpath (fitting error) is \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Methods** & **Kinematics** & **X** & **Y** & **Z** & **A** & **C** \\ \hline \multirow{3}{*}{Without constraints} & Velocities(mm \(\mid\) rad/s) & 98.9 & 72.7 & 68.6 & 0.39 & **0.56** \\  & Accelerations(mm \(\mid\) rad/s\({}^{2}\)) & **2795** & 607.3 & 569.3 & 2.67 & 2.45 \\ \multirow{3}{*}{Parameter synchronization} & Velocities(mm \(\mid\) rad/s\({}^{2}\)) & 91.2 & 65.6 & 66.4 & 0.29 & 0.5 \\  & Accelerations(mm \(\mid\) rad/s\({}^{2}\)) & **2871** & **4781** & **2929** & **44.4** & **44.1** \\ \multirow{3}{*}{Proposed} & Velocities(mm \(\mid\) rad/s\({}^{2}\)) & 91.2 & 64.6 & 66 & 0.29 & 0.49 \\ \cline{1-1}  & Accelerations(mm \(\mid\) rad/s\({}^{2}\)) & 828.5 & 493.4 & 513.4 & 1.62 & 1.71 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Maximum axial velocities and accelerationsusing different methods. Figure 19: Machining results of S-shaped workpiece. Figure 18: Proposed axial velocities and accelerations—(**a**) Translational axial velocities. (**b**) Rotary axial velocities. (**c**) Translational axial accelerations. (**d**) Rotary axial accelerations. mainly concerned. According to drive commands of the machine tool, the real tool-tip position and tool orientation are reversely computed using forward kinematic transformation and the real fitting error in the WCS is calculated. The real maximum and average fitting errors with the traditional parameter synchronization method and the proposed method are demonstrated in Table 3. Due to large variance of axial kinematics, the maximum and average fitting errors with the traditional parameter synchronization show greater values, especially for the tool orientation (40.0% greater for the maximum fitting error). The real axial velocities and accelerations shown in Figure 20 are obtained by differentiating the real axial positions with the sampling period of 1 ms. The real axial velocities and accelerations are both under control and the large fluctuations are further reduced in the real machining process (the acceleration for X-axis is reduced by 44.3%). Experimental results show that the proposed method can generate trajectories with allowable fitting error and smooth motion.
# Zeroes: Robust Derivative-Based Demodulation Method for Optical Camera Communication
## 3 Method Description  
While several demodulation methods have been implemented to determine the width of bright and dark strips, dealing with the blooming effect through adaptive thresholds or first-order derivative, none of the proposed methods, to the best of authors' knowledge, remain robust enough when the environment is becoming more complex. Hence, a new demodulation method is proposed with the aim of achieving greater robustness within complex environments in order to enhance OCC systems' performance, therefore extending the range of possible applications. As illustrated in Figure 4, the proposed ZEROES method is based on a first-order derivative approach for the demodulation of the extracted signal (\(x\)). Indeed, the latter is firstly filtered and normalized (\(x_{n}\)) in order to prepare and enhance the derivative computation. Then, from the peaks and valleys (\(PV\)) of the derivative (\(x_{n}^{\prime}\)), representing its local maximums and minimums, associated zeros are deduced (_zeros_), where the slope of the extracted signal is constant, leading to the identification of the extracted signal boundaries (_bnd_). Finally, the switching point (_switch_) of the logical state is determined from the boundary positions by computing the intersection point between the signal and the constant straight line whose value is the average of the boundary points. Thus, the accurate detection of the signal boundaries through the zeros' values of the derivative, combined with the optimal identification of the logical state switching points, therefore provides great robustness in demodulation with ZEROES.  
Figure 3: Illustration of the IFG.