esa-sceva/satcom-chunk-collection huggingface dataset:
# Opportunistic Network Algorithms for Internet Traffic Offloading in Music Festival Scenarios
## 5 Evaluation
### Results
#### 5.3.1 Bluetooth Trace  
Figure 8a shows the delivery rates obtained by the analyzed algorithms for the Bluetooth trace. While most of the solutions tested have high delivery rate values in general, SF and Bubble Rap did not reach peak performance. SF proves that using last-encounter timestamps in a concert scenario is not a good strategy due to the lack of structure or repeating patterns. A node may choose another node as a relay for a message, even though it had recently encountered the message destination by chance and their paths are not likely to cross again. Moreover, the mechanism Bubble Rap uses for community detection is not reliable in the studied scenario. A node \(A\) is accepted as a member of the local community of another node \(B\), provided that they have been in contact for a period of time which exceeds a threshold, or the familiarity set of node \(A\) contains enough community members of node \(B\). Although this may work in most cases, a crowded event like a concert can mislead the algorithm to establish communities between nodes that have been staying next to each other, but have no social relationship in real life. There is a good potential for improving Bubble Rap with this knowledge, and we plan on doing so as future work.  
Figure 7: Contact duration distribution for the Wi-Fi Direct trace.  
As expected and as shown in Figure 7(b), Epidemic has the highest overhead (due to its flooding-based mechanism), followed closely by SWs, whereas SWv6 might have too many constraints which prevent it from delivering the messages efficiently. Figure 7(b) narrows down our search for the most suitable opportunistic algorithm to be deployed at a music festival to three candidates: SWb, SSW1, and SSW2. For SSW2, we have performed the tests with two threshold values for centrality, each value corresponding to a version of the algorithm in the charts. SSW2v1 used a threshold of 50, while SSW2v2 used 100.  
Figure 8(a) shows that, although there are other protocols with better delivery delay values than our selected algorithms, high overheads or low delivery rates prevent them from being considered. SWv5 has a comparable delivery rate and delivery delay to SSW1, but its more than double overhead disqualifies it from being taken into consideration. Furthermore, all three candidates have delays below 5 min, which should be considered an acceptable time in a delay-tolerant network.  
Finally, Figure 8(b) shows that SWb has a lower hop count than the other two candidates (SSW1 and SSW2), aside from its slightly lower overhead. However, we have considered delivery delay to be the most essential criterion, so we can conclude that all three algorithms perform well in a festival scenario. While SWb may be the better choice if we want optimal resource usage, SSW1 and SSW2 will lead to lower delivery delay values with an insignificant extra cost.
## 3 Results and Discussion
### Beam Focusing  
The other important issue of wavefront control equipment is beam focusing, which has many practical applications. The results above have already demonstrated the ability of the designed elements to actively manipulate the phase of the radiation field. According to the Huygens-Fresnel principle, we can also obtain beam focusing by engineering the phase distribution in the way of focusing formula, which can be described by the following equation [39]:  
\[\varphi(\mathbf{r})=\frac{2\pi}{\lambda}\Big{(}f-\sqrt{\mathbf{r}^{2}+f^{2}} \Big{)}, \tag{2}\]  
where \(f\) is the focal length of the array, \(\lambda\) is the wavelength of electromagnetic wave, \(\mathbf{r}\) is the distance vector between the array elements and the focal point, and \(\varphi(\mathbf{r})\) is the phase profile of each array element. Equation (2) implies the required phase of each array element for beam focusing at a certain point. In our scheme, we chose to focus at a distance of five times the wavelength from the center of the patch array, which means \(f\) = 5 mm. In addition, we planned to manipulate the focus at different positions in the \(y\)-\(z\) plane for demonstrating the good performance of the tenability. When \(\mathbf{r}\) and \(f\) are determined, the corresponding phase value of each element can be obtained by substituting them into Equation (1), as shown in Figure 6(a1-a3). The electric field distributions in the \(y\)-\(z\) plane are plotted in Figure 6(b1-b3). It is not difficult to observe the focusing of the radiation field at the distance of \(f\) = 5 mm. In addition, the focus is actively tuned on the left, middle, and right sides of the \(y\)-\(z\) plane. The simulated electric field intensity at the focus in the \(x\)-\(y\) plane is shown in Figure 6(c1-c3), further validating the active tenability of the focusing property. Figure 6(d1-d3) plot the electric field intensity extracted along the red lines in Figure 6(b1-b3), respectively. The focus phenomenon is significant even though the sidelobe level is similar to the main focusing peak. We have to say that the focusing property is not good enough due to the discontinuous phase distributions, as anyone can be seen from Figure 6(a1-a3). Nevertheless, this work considers the tunable manipulation of the wavefront which has been successfully demonstrated. A better focusing property could be achieved by designing a more accurate phase gradient by a better manipulation of the feeding lines of the unit element.  
Figure 6: The phase distribution for beam focusing on the (**a1**) left, (**a2**) middle and (**a3**) right sides of the y-\(z\) plane. (**b1–b3**) and (**c1–c3**) show the electric field intensity distribution focusing on the left, middle and right sides in the \(y\)-\(z\) and \(x\)-\(y\) plane, respectively. (**d1–d3**) The extracted field intensities along the red line in (**b1–b3**).
## 7 Antenna Qualification and Acceptance Test
### Shock Test  
The objective of the final mechanical test is to ascertain the capacity of the Leader-S QM S Band antenna to withstand mechanical shock requirements and thereby bring the mechanical tests performed on the QM unit to a conclusion. Once again, the QM unit antenna remains mounted on the mass dummy and is now mounted inside the ISIS TESTPOD in the same manner as in the vibration tests (see Figure 21e). The g-acceleration values to which the antenna is subjected are described in Table 5, and a safety factor of +6 dB is applied to this profile for qualification tests (as required by the launcher, SpaceX's Falcon 9). Finally, three actions on each axis are performed, and the equipment for testing is located in a cleanroom class ISO 8.  
S-parameter measurements are taken after each of the environmental tests to ensure there is no distortion of the S-parameters and, therefore, to guarantee that the antenna maintains its electrical functionality.
## 2 Related Work
### Multi-Camera Cross-Region Trajectory Splicing  
Currently, the main method of vehicle trajectory splicing is finding the trajectories of the same vehicle in different videos and then splicing the trajectories. For vehicle re-identification, the basic method is based on sensors, such as using wireless magnetic sensors to determine vehicle identity [34]. With the development of emerging sensors and technologies, such as identification tags and GPS (global positioning system), vehicles can be re-identified directly. For example, [PERSON] proposed a vehicle re-identification algorithm based on radio-frequency identification tags, which is suitable for various toll stations [35].  
Considering the low penetration of emerging sensors and the large-scale use of visual sensors on the road, many studies have proposed methods to realize vehicle re-identification from videos. The early method is based on the method of artificial design features [36; 37]. Researchers directly extract specific vehicle features from vehicle images to calculate the similarity to re-identify the vehicle ID (specific vehicle features: vehicle texture, vehicle color distribution, etc.), such as the BOW-SIFT method based on texture features and the BOW-CN method based on color features proposed by [PERSON] et al. [38]. After CNN (convolution neural network) was suggested, scholars proposed using deep learning to obtain multidimensional information about vehicles to measure the similarity between vehicle images [39; 40; 41; 42; 43]. Simultaneously, considering the spatial and temporal information of vehicles, scholars have proposed methods such as screening time and geographical correlations to increase the accuracy of vehicle re-identification [44; 45]. Although the current models have high accuracy, they struggle to solve some problems in practical applications, such as the appearance of multiple similar vehicles simultaneously. Therefore, in the framework of vehicle trajectory reconstruction, the vehicle re-identification model needs improvements to overcome these practical problems.
# A Perfect Match: Converging and Automating Privacy and Security Impact Assessment On-the-Fly
## 4 APSIA Conceptual Architecture and Building Blocks  
The iterative steps of the APSIA methodology aim to keep the enhanced assessment up-to-date and consider any changes that may (dynamically) occur in the Cyber, Physical and Operational environment of the target ecosystem. APSIA is capable to support and complement PIA procedures and to enhance the assessment with dynamic asset inventory and vulnerability discovery techniques in order to feed the assessment steps dynamically. Figure 3 presents the high-level architecture of APSIA, which highlights the separation between the physical environment and the APSIA operational environment, while intuitively reflects the interconnections that enable the flow of information to the building blocks of APSIA.  
_Physical Environment:_ APSIA takes advantage of information regarding the tangible and intangible assets (along with their interdependencies), the different actors, threats and vulnerabilities, and the organisation's processing activities. The discovery of ICT assets and vulnerabilities is achieved through the use of inventory tools (OpenVAS), while the documentation of the processing activities is part of the initial configuration of APSIA by the CISO or DPO of an organisation, who is aware of the data and processes that should be in the scope of the assessment.  
_APSIA Operational Environment:_ The APSIA environment incorporates the necessary building blocks for performing the assessment. More specifically, the internal modeling components of APSIA generate the interdependency graph and identify the chain of assets included in the processing activities. The interdependency graphs assist the security analyst,  
Figure 3: APSIA Conceptual Architecture.  
to identify potential privacy risks based on a cartography of assets, which encapsulate their vulnerabilities and the potential privacy threats posed against them. This graph is updated every time a new assessment is conducted in order to include possible updates from the assessed ecosystem. The interdependency graphs are also used for constructing the data processing flows, which are formed through the asset paths and data sources defined by the assessor. After defining the aforementioned dependencies and having an updated reflection of the assessed environment, the Privacy scoring system undertakes the quantification of the privacy threats. To do so, the scoring system is based on risk factors of existing repositories, such as CVE, but can be extended by other context dimensions.  
The following sections elaborate on each of the APSIA building blocks by providing more details on their structure and functional behavior.
## 3 Results and Discussion
### Comparison of Grinding Characteristic between Untreated and Pre-treated Converter Steel Slag  
From the research result presented in the previous section, the iron-rich phases, especially metallic iron, are the main hardly grinding phases, so it is necessary to remove or recycle the iron-rich phases before the grinding process of steel slag. In this study, the grinding characteristics of untreated and pretreated converter steel slag were compared with respect to iron mineral phases, grinding efficiency, particle size distribution, and particle morphology (Comment: the pretreatment of steel slag is the preliminary magnetic separation and multistage screening and magnetic separation, as shown in Figure 5).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Region number** & **1** & **2** & **3** & **4** \\ \hline Mineral phases & Silicate phase & Calcium ferrite phase & RO phase & Metallic iron phase \\ Vickers hardness & 187.3 & 335.0 & 298.1 & 29.4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Vickers hardness of selected region in Figure 4 (HV).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Grinding time (min)** & **CaO** & **SiO\({}_{2}\)** & **Al\({}_{2}\)O\({}_{3}\)** & **Fe\({}_{2}\)O\({}_{3}\)** & **Fe** & **MgO** \\ \hline
10 & 32.45 & 12.05 & 1.15 & 34.80 & 3.53 & 8.43 \\
20 & 26.12 & 8.26 & 1.06 & 37.07 & 7.60 & 12.29 \\
30 & 20.30 & 5.81 & 0.92 & 38.13 & 13.36 & 13.07 \\
40 & 14.70 & 4.16 & 0.75 & 39.08 & 23.10 & 10.13 \\
50 &
# Smart Beamforming for Direct LEO Satellite Access of Future IoT
## 5 Numerical Results
### Signal-to-Interference-Plus-Noise Ratio
As the fixed beamforming network operates with full frequency reuse and adjacent beams are overlapped, the signal of a given user leaks into several beams. Due to the noise and the interference, the system may not choose the closest beam to decode the signal, so that it could be received from the side lobe of a neighboring beam. We have disregarded this situation and thus, the SINR is computed assuming that the signal is extracted from the beam with the highest gain. It is  
Figure 10: User positions and 4.3 dB contour of the beams generated by the proposed beamforming with a planar array of \(24\times 24\) antenna elements. Figure 9: SINR versus \(N_{\text{U}}\) in fixed and digital beamforming schemes for \(N_{x}=N_{y}=24\). noteworthy to mention that the fixed beams grid approach is evaluated to gain a better insight into the benefits of RBSA, but it is not optimized. The best performance in the context of fixed beamforming will require some radio resource management- (RRM) based assignment to avoid users that are too physically close and are simultaneously transmitting. However, this is not consistent with the GF philosophy and thus, it is not applicable for unscheduled transmissions. Alternatively, the SINR gain could be increased for current terminal offset from the beam center, by having larger beams overlap. The maximization of the SINR in the scenario under study for fixed beamforming matrices is an interesting line of work that will be targeted in the future.
# Chloroplasts at the Crossroad of Photosynthesis, Pathogen Infection and Plant Defense
## 5 The Complex Relationship between Photosynthesis and Defense-related Signals  
Extensive interactions exist between photosynthesis and defense-related signals (Figure 2). Photosynthetic electron transport supplies electrons to ROS producing enzymes; more importantly, photosynthesis provides NADPH, ATP, and carbon skeletons for the synthesis of defense-related compounds, including ABA, ET, JA, SA, and even -NO (Figure 2). These defense-related hormones and signaling molecules may in turn influence photosynthesis (Figure 2). ABA, JA, and -NO tend to have negative impacts on photosynthesis. Exogenous treatments with these molecules caused stomatal closure (or delayed stomatal opening) and reduced photosynthesis (or photosynthetic pigments) [61; 62; 63; 137; 249; 250; 292]. ET, SA, and ROS may have positive and negative impacts on photosynthesis. The effects of ET on photosynthesis are species-specific and age-dependent [110]. Similarly, the effects of SA on photosynthesis depend on plant species, treatment methods, treatment durations, and growth conditions [155]. The effects of ROS on photosynthesis also multifaceted. On the one hand, ROS production by the photosynthetic electron transport chain has a protective role over photosynthetic complexes [15]. On the other hand, excess amounts of ROS can damage photosynthetic complexes, especially PSII, and thus result in photoinhibition [15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 293]. Ca\({}^{2+}\) generally has a positive impact on photosynthesis. The thylakoid membrane-localized CAS and stromal Ca\({}^{2+}\) spikes are essential for the regulation and photoacclimation of photosynthesis [278].  
Figure 2: Interactions of photosynthesis and defense responses with defense–related signals. Red dotted arrows represent that photosynthesis provides electrons, NADPH, ATP, and/or carbon skeletons to the biosynthesis of defense hormones and other signals. Red arrow heads represent positive (promoting) effects; blue bars represent negative (inhibitory) effects; grey lines with red arrow heads and blue bars represent both positive and negative effects.
# Virtual Pseudonym-Changing and Dynamic Grouping Policy for Privacy Preservation in VANETs
## 9 Analysis and Discussion
### DGVP Scheme Impact on VANET Applications  
The privacy protection mechanism has some impact on VANET applications. The user wants to hide the private information related to his/her location while also wanting to efficiently manage the vehicular network applications. The proposed scheme, DGVP, uses a neighbor's data to anonymize the identity of the target vehicle. The pseudonyms of vehicles are updated in a grouped manner. Our privacy scheme provides both location and identity protection to the vehicle while considering the smooth function of VANETs applications. We do not change the relative traffic information broadcast during a vehicle journey; however, the vehicles' meeting areas are explored to obtain an opportunity for protection of location privacy of the target vehicle. The proposed scheme creates confusion for an adversary to identify a vehicle in a group and does not change any information related to VANET applications such as safety and infotainment applications. We take the expectation of an adversary confusion in terms of anonymization of vehicles in a group [35].  
\[E(Anonymity)=\frac{1}{MN}\sum_{i=1}^{M}\sum_{t=1}^{N}P_{i}(t)[(G_{ID_{i}}(t), PseudoUpdate_{i}(t))V_{i}]. \tag{60}\]  
where \(P_{i}(t)\) is the probability of an adversary to identify a target vehicle in the group. \(G_{ID}\) is the group identity assigned at the time of group creation, \(PseudoUpdate_{i}\) is the vehicle pseudonym-changing during the crowd of vehicles, \(V_{i}\) is any vehicle moving on the road, \(M\) is the number of the vehicles taking part in the anonymization process, and \(N\) is the observation time of vehicle data in the vicinity. The quality of applications (QoA) can be defined in terms of confusion or uncertainty as given in the following equation:  
\[QoA=\frac{1}{MN}\sum_{i=1}^{M}\sum_{t=1}^{N}[VPseudo_{i}(t),G_{ID}(Pseudo_{ i}(t),Pseudo_{i}^{\prime}(t))]V_{i}. \tag{61}\]  
where \(VPseudo_{i}(t)\) is the virtual pseudonym used during a lower number of vehicles in a group, \(Pseudo_{i}(t)\) is the initial pseudonym of vehicle and \(Pseudo_{i}^{\prime}(t)\) is the change in the pseudonym of a vehicle after time \(t\). In Equation (61), it is clear that the proposed scheme creates confusion for an adversary to link the various pseudonyms of the vehicle at different periods. However, the parameters used for the anonymization of vehicles did not  
Figure 25: Adversary confusion in identifying vehicles on the road.  
contain any distortion data that reduces vehicular application service quality. Hence, the proposed scheme has a lower impact on the quality of VANET applications.
## 4 Agent Training with Markov Decision Process (MDP)
### Design of the MDP Model  
In the AWR-PF, we formulate the agent's observable state space by integrating multi-dimensional information extracted from four layers: satellite, epoch, signal, and positioning. The action space is systematically constrained within a scientifically determined weighting range to ensure operational validity. A dynamic reward mechanism is designed to precisely quantify positioning error variations, while the state transition process is meticulously designed to reflect system dynamics. This integrated approach yields a mathematically rigorous MDP model that enables comprehensive epoch validity assessment for optimal weighting decisions. The proposed model establishes a theoretical foundation for intelligent epoch-weight optimization in advanced LEO SOP positioning systems.
# Experimental Evaluation of a Takagi-Sugeno Fuzzy Controller for an EV3 Ballbot System +
## 4 Results  
### LEGO Mindstorms EV3 Hardware  
The MATLAB(r) and Simulink (Simulink 9.1)(r) environments contain freeware tools for programming and communicating with LEGO Mindstorms EV3 technology (LEGO Group, Billund, Denmark). The acronym EV3 refers to the word _Evolution_, while the number represents the generation of the set. The brain of the LEGO Mindstorms EV3 kit is also known as _smart brick_, which contains a 300 MHz ARM9 microcontroller (Texas Instruments, Dallas, TX, USA ) that collects information from sensors, processes it, and sends commands to the actuators. The smart brick can communicate with computers, smartphones, and other robots thanks to USB, Bluetooth, and WiFi connectivity. This brain has 16 MB flash memory, 64 MB RAM (Offtek Ltd., Solihull, UK), and a Linux operating system.  
For the control of the EV3 BRS, the _Motor_, _Gyro Sensor_, and _Encoder_ (rotation sensor) Simulink(r) blocks are used, available from the Simulink(r) support package for LEGO Mindstorms EV3 hardware. Figure 5 shows the blocks related to the I/O peripherals. The input from the LEGO EV3 _Motor_ block accepts integer values between \(-100\) (full power reverse) and \(+100\) (full power forward); i.e., the input voltage (\(\pm 9\) [V]) from each motor is translated to a pulse width modulator (PWM) duty cycle with values within the range of \(\pm 100\%\). The brick output port that is connected to each motor must be selected from the same Motor block parameters. In addition, the LEGO EV3 _Encoder_ block allows the measurement of the cumulative rotation of a motor in degrees [deg]. The angular displacement of the EV3 BRS' body from its upright position is obtained through a LEGO EV3 _Gyro Sensor_ located in each plane, which provides the measure of rotation in degrees [deg] around a single vertical axis that extends from the top and bottom of the same gyro sensor. The angular velocity is calculated through a Derivative Simulink(r) block.  
The Simulink(r) support package for LEGO Mindstorms EV3 hardware is easy to use since each block works like any other Simulink(r) block. When the code is completed, Simulink(r) translates it into the C++ code and transfers it to the LEGO Mindstorms EV3 brick via Bluetooth, WiFi, USB, or ethernet connection. One of the main difficulties in achieving the connection between the brick and Simulink(r) is to find the appropriate firmware version for the LEGO EV3 block that is compatible with the version of MATLAB(r) being used. In this work, the connection between the brick and the host computer running MATLAB(r)2018a/Simulink(r) is established by way of WiFi access via the brick's USB port, for an execution time of 20 s and a sampling time of 0.01 s. Figure 6 shows the hardware connections used to carry out the experimental evaluation.  
Figure 4: TSFC scheme.  
Figure 3: Membership functions.