esa-sceva/satcom-chunk-collection huggingface dataset:
# Secure Voting Website Using Ethereum and Smart Contracts
## 4 Proposed System
### Firebase Integration  
One of the critical aspects of the proposed blockchain-based voting system is the secure storage and management of voter registration data. This data contains sensitive information, including voters' details and unique identification numbers. Any unauthorized access or tampering of this data could compromise the entire election process. In the proposed blockchain voting system, when voters register to participate in an election, their registration data is collected and stored securely on Firebase Cloud. This data includes the voter's name, address, age, and other relevant details required to verify their voting eligibility. When voters attempt to vote, the blockchain verifies their identity by accessing their registration data on Firebase Cloud. The blockchain checks the voter's ID and cross-references it with their personal information to determine that the voter is eligible to cast their vote. If the information matches, the voter is allowed to proceed with casting their vote. Furthermore, integrating Firebase Cloud with the blockchain voting system also offers other benefits. For instance, Firebase Cloud's scalability ensures the system can handle a large volume of voter registration data. This feature is essential in countries with a large voter population where traditional voting systems need help to cope with the sheer number of voters. Moreover, Firebase Cloud's accessibility is another advantage that it brings to the table. It enables voters to register for voting from anywhere worldwide, as long as they have an internet connection. This feature increases the convenience of the voting process, making it more accessible to a broader range of people. It provides a secure and scalable platform for storing voter registration data and allows the blockchain to verify the identity of voters before they can cast their votes. The real-time updates feature ensures the data is up-to-date and accurate, enhancing the system's security. Finally, the accessibility of Firebase Cloud makes the voting process more convenient for voters, increasing the overall participation rate [24; 25].
## 1 Introduction  
The use of communications for the transmission of information in the field of transport allows the introduction of services that increase safety and efficiency, both at the vehicle and mobility levels. At present, there are already several initiatives of cooperative services and cooperative corridors. In addition, various technologies coexist, where each one provides different and complementary properties, such as DSRC-based communication modules and cellular technology with 3, 4, and 5G.  
On the other hand, services that are in a state of greater maturity are designed as C-ITS Day-1 and Day-1.5. Demonstrations are presented in small-scale trials, concept tests, and Field Operational Tests. These tests allow one to obtain information on the main limitations and opportunities of these services, as well as the viability of the technologies and information flow architectures that are proposed.  
On the other hand, aspects such as the standardization of communications and information transmitted, as well as solutions to guarantee security, are relevant.  
This Special Issue aims to cover the most recent advances in connected vehicles, C-ITS, and the technologies involved.
# Real-Time Image Detection for Edge Devices: A Peak Fruit Detection Application
## 3 Results and Discussions
### Accuracy and Inference Time Trade-Off  
In Sections 3.1 and 3.2, the accuracy (AP) and inference time (ms) of the models for the target TPU device were presented. The models designed specifically for TPU devices had a better detection accuracy, and those designed specifically for CPU (but optimized for TPU) had a better inference time. Thus, there was a trade-off between the accuracy and latency, as shown in Figure 5. Comparing the fastest model (SSD MobileNetV1) with the model that had the best detection accuracy (SSD MobileDet), there was a gain in detection accuracy of 4.4% at the expense of a loss in inference time of 2.8 ms (equivalent to a loss of 1.17 FPS). At a loss of 1.17, the FPS did not significantly affect the performance in the practical applications of computer vision. Therefore, it is justifiable to use SSD MobileDet to improve the recognition accuracy.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Latency**} \\ \cline{2-4}  & **CPU (ms)** & **EdgeTPU (ms)** & **FPS** \\ \hline SSD MobileNetV1 & 847.9 & 47.6 & 21.01 \\ \hline SSDLite MobileDet & 1045.9 & 50.4 & 19.84 \\ \hline MobileNet EdgeTPU & 1232 & 50.5 & 19.80 \\ \hline SSD MobileNetV2 & 773.1 & 48.4 & 20.66 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Inference time comparison.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**AP (\%)**} \\ \cline{2-4}  & **Baseline** & **EdgeTPU** & **Drop from Baseline to TPU** \\ \hline SSDLite MobileDet & 89 & 88.2 & 0.8 \\ \hline MobileNet EdgeTPU & 88 & 87.5 & 0.5 \\ \hline SSD MobileNetV2 & 86 & 84.5 & 1.5 \\ \hline SSD MobileNetV1 & 85 & 83.8 & 1.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Target hardware comparison.  
The performance of the SSD MobileDet model presented in this study was compared with the results of other studies. The results are shown in Table 5. Given the lack of practical applications in horticulture for the fruit detection task [22], this comparison provides insight into model performance, edge devices, and price (cost).  
Approach 1 was the cheapest and most accurate; however, the combination of the model and device led to a very high inference time. Approach 2 was the most expensive--almost four times the cost of the cheapest--and the least accurate. Nevertheless, they had the best inference times.  
Our approach was inexpensive and had a cost similar to that of Approach 1. The accuracy was better than that of Approach 2, but worse than that of Approach 1. The inference time was slightly lower than that of Approach 2, but much better than that of Approach 1. A direct comparison between the approaches in Table 5 was not possible because different datasets and image sizes were used. Considering the price, AP, and latency, our approach of using a TPU accelerator was a good alternative for practical applications.
## 2 Graphene and Its Derivatives: Graphene Oxide and Reduced Graphene Oxide  
### Chemical Structure of Graphene, GO, and rGO  
In a graphene sheet, three out-shell electrons of a carbon atom occupy three sp\({}^{2}\) hybrid orbitals, i.e., s, p\({}_{\text{x}}\), and p\({}_{\text{y}}\), which are shared with the three nearest atoms, forming \(\sigma\)-bonds (\(\sim\)0.14 nm). The remaining outer-shell electron occupies a p\({}_{\text{z}}\) orbital that is oriented perpendicularly to the plane (Figure 2a) [21; 22]. These orbitals hybridize together to form two half-filled bands of free-moving electrons, \(\pi\), and \(\pi\ast\), which are responsible for the unique electronic properties in graphene [23].  
In the graphene derivatives, GO and rGO, the obvious difference between these materials is the addition of oxygen atoms bound with the carbon scaffold (Figure 2b). The GO contains both aromatic (sp\({}^{2}\)) and aliphatic (sp\({}^{3}\)) domains, further expanding the types of interactions that can occur with the surface. Additionally, GO is easily reduced to rGO at high yields (Figure 2c) and can be partly reduced to graphene-like sheets by removing the oxygen-containing moieties with the recovery of a p-conjugated structure [24]. Furthermore, these modified graphene show different chemical and structural properties due to the differences in their chemical compositions (Figure 2b,c). As a result, the most notable differences of GO and rGO are observed in the electrical conductivity, hydrophilic  
Figure 1: Graphene-based radio-frequency (RF) nanodevices and circuits. (**a**) Graphene: a single atomic sheet of sp2-bonded carbon [\(\mathcal{T}\)]; (**b**) graphene-based radio-frequency identification (RFID) tag antenna [8]; (**c**) graphene-based frequency mixer; and (**d**) graphene-based 100 GHz transistor [9].  
behavior, mechanical strength, and dispersibility of these materials. These physical and chemical properties are utilized in energy storage applications [25; 26], sensors [27; 28], supercapacitors [29; 30], solar cells [31; 32], and biomedical applications [33; 34].
## 10 Targeted Injury Prevention: The Role of Single-Component Programs in Youth Female Soccer  
An alternative to multi-component programs, particularly for addressing secondary prevention aimed at minimizing the impact of an existing injury, is single-component programs. These programs target specific objectives, such as eccentric hamstring strengthening or balance training. While their narrower focus limits their scope compared to multi-component programs, they have demonstrated effectiveness in reducing injury rates, achieving a 22% decrease in overall injuries and a 38% reduction in ACL injuries [105]. Despite their more restricted approach, single-component programs play a valuable role in preventing specific types of injuries, making them a strategic option in cases where targeted interventions are necessary.  
[PERSON] et al. [75] highlighted the benefits of eccentric hamstring exercises performed twice per week for 10-15 min, achieving a notable reduction in hamstring injuries (1 vs. 5) among players in the first and second national divisions. These findings align with previous evidence indicating that eccentric hamstring exercises can reduce injury ratesby 45% to 77%, provided that there is high adherence to the program [120]. Increasing eccentric muscle strength not only reduces the incidence of hamstring strain injuries but also enhances the role of the hamstring muscles as dynamic agonists to the ACL, particularly during high-demand actions such as stopping and jumping [121]. By generating protective forces, especially at knee flexion angles above 30\({}^{\circ}\), stronger hamstring muscles contribute to knee stabilization, potentially reducing the risk of ACL injuries [122]. Although the direct relationship between hamstring strength and ACL injury prevention remains to be conclusively established, the biomechanical benefits provided by improved hamstring function highlight the critical role of eccentric strength training in comprehensive injury prevention programs.  
Similarly, [PERSON] et al. [123] evaluated a home-based balance training program consisting of daily sessions for 30 days, followed by three weekly sessions for the remainder of the season, with each session lasting 10-15 min. This intervention resulted in modest reductions in knee injuries (8 vs. 6) and hamstring injuries (1 vs. 7) but also showed a slight increase in ACL injuries (4 vs. 1), emphasizing the need for more targeted strategies to address ACL-specific injury mechanisms.  
Overall, single-component programs demonstrate meaningful injury prevention benefits when designed to target specific biomechanical risks or tailored to the demands of particular sporting contexts. However, combining these programs with multi-component strategies may offer a more comprehensive approach to reducing injury rates and enhancing athlete safety.
# Mapping [PERSON] Using Sentinel-1 SAR Time Series in Camargue, France
## 3 Methodology
### Temporal Behavior of \(\sigma^{\circ}\) SAR Backscattering over Agricultural Plots
#### 3.1.1 Gaussian Fitting of VV/VH  
The profile of the time series for VV/VH follows a Gaussian bell shape for the rice plots. This characteristic is especially observed over rice cultivation. For this reason, we proposed fitting the VV/VH time series of each plot with a Gaussian function. First, the time series values of VV/VH for each reference plot were normalized using the unity-based normalization in Equation 2 to adjust the values at a common scale (between 0 and 1).  
\[Y^{\prime}=\frac{Y-Y_{min}}{Y_{max}-Y_{min}} \tag{2}\]  
where \(Y^{\prime}\) is the normalized value, \(Y\) is the initial value of the time series, \(Y_{\rm min}\) is the minimum value of the time series, and \(Y_{\rm max}\) is the maximum value of the time series.  
To avoid small and noisy peaks of the raw VV/VH data (black curve in Figure 6), the temporal series of the VV/VH ratio was smoothed for each reference plot using a Gaussian filter (blue curve in Figure 5). The smoothed data allowed us to estimate the peak position and amplitude required to initialize the Gaussian fitting. In fact, the Gaussian fitting with the least square method requires the initial values of the peak position and amplitude. Using the least square method, the VV/VH time series was then fitted with a Gaussian function presented in Equation 3 (red curve in Figure 6):  
\[y=a\ e^{-\frac{(x-b)^{2}}{2\alpha^{2}}} \tag{3}\]  
where \(a\) is the amplitude, \(b\) is the position of the maximum in days, and \(c\) is the standard deviation of the fitting function in days.  
For each plot we can then obtain the resultant fitting parameters \(a\), \(b\), and \(c\), as well as the correlation coefficient \(R^{2}\). Since the rice crop is the only crop among the 11 studied crops that follows the Gaussian profile in VV/VH ratio, it is possible to distinguish the rice plots using the obtained fitting parameters.
# Influence of Load Dynamics on Converter-Dominated Isolated Power Systems
## 5 Control Strategies for the Mitigation of the Impacts Induced by Induction Motor Loads  
The results presented in Section 4 highlight that large shares of IM loads are responsible for an increased post-fault reactive current contribution from the BESS power converter required for the re-excitation of the IM loads (which can be observed in Figure 9). An immediate solution for this problem may consist in increasing the sizing of the BESS power converter, as it was also illustrated in the previous section. Nevertheless, alternative solutions based on the exploitation of advanced control strategies for the available power electronic interfaces are proposed in order to minimize the impacts that IM loads may bring, during the post-fault recovery period, with respect to BESS power converter oversizing.  
An alternative and simple solution to mitigate this issue consists of adopting undervoltage IM load shedding mechanisms [33]. However, in this work, it is intended to propose solutions which ensure the complete recovery of all the network IM loads while profiting from the control flexibility available in all the power electronic interfaces. Therefore, two distinct approaches are proposed. The first approach consists of implementing appropriated control solutions at the CI-RES, aiming to increase their ability to provide a post-fault reactive current to re-excite the IM loads and hence support the grid (this control solution is presented in Section 5.1). Complementarily, the second approach consists of a dynamic modulation of the reference voltage of the grid-forming unit during the post-fault period, aiming to increase the post-fault electromagnetic torque in IM loads and hence contribute to its reacceleration (this solution is presented in Section 5.2). In order to evaluate the effectiveness of the proposed solutions, the operational scenario presented in Section 4.2.1 is considered, where the BESS power converter capacity is assumed to be 12 MVA. A 100 ms three-phase symmetrical short circuit reference incidente occurring at t = 0 s of the simulation time in the transmission line interconnecting buses \(B_{7}\) and \(B_{8}\) (see Figure 6) is considered. Finally, in Section 5.3, it is demonstrated that the coordinated implementation of these two control solutions is beneficial in operating scenarios with very large shares of IM loads.
## 3 Classification of Optical Waveguide Sensors
### MZI-Structured Optical Waveguide Sensor  
MZI-structured sensors are common label-free optical sensing devices. Their structure is relatively simple, easy to integrate, and can be used with special materials or combined with other flexible optical devices. The detection limit is generally between \(10^{5}\) RIU and \(10^{7}\) RIU. It has the advantages of high refractive index sensitivity, strong anti-interference ability, and high detection efficiency and exhibits good research significance and application value in biochemical sensing.  
Optical waveguide sensors, such as optical waveguide temperature sensors, stress sensors, and various optical waveguide biochemical sensors, are research hotspots worldwide. Research on optical waveguide biochemical sensors has primarily focused on surface plasmon resonance and waveguide grating sensors. These sensors exhibit high sensitivity and response recovery characteristics. However, selectivity is poor, and the anti-interference ability is weak. The MZI optical waveguide sensor has the advantages of high sensitivity, strong selectivity, anti-interference ability, and small size. According to previous reports, the phase-difference resolution of the MZI optical waveguide sensor can reach \(5\times 10^{-5}\times 2\pi\), and the resolution of the refractive index change is approximately \(2\times 10^{-8}\). The detection of the lowest concentration of specific analytes was \(10^{-6}\). The transmission loss of the optical waveguide is required to be less than 1 dB.  
MRRs and MZIs are two common configurations used in waveguide-based sensors for optical signal readout. MRR biochemical sensors exhibit high sensitivity during the detection of proteins, DNA, viruses, and bacteria [9]. Wavelength-tunable lasers and spectral analyzers are required to perform wavelength scanning, and the detection limit of MRR-based sensors is limited by the resolution of expensive and complex external devices. By contrast, the MZI readout configuration is based on the detection of light intensity and does not require tunable lasers or optical spectrum analyzers. Therefore, miniaturization and integration of an optical waveguide MZI as a sensing platform system is feasible.
## 2 Related Work
### COST C21 Action  
The main objective of the European COST C21 [10] Action, known informally as \"Towntology\" [12], which brings together a large group of experts from across Europe, is to \"increase knowledge of and promote the use of ontologies in the domain of urban development, with a view to facilitating communications between information systems, stakeholders, and urban experts at European level\" [17]. The research work of this group is mainly guided by the assumption that urban practice is inherently based on shared conceptualisations, even though these may be implicit. These conceptualisations can be regarded as ontologies although they remain in the pre-formalisation stage. They are also known as pre-ontologies. The work of the COST C21 is to reveal such underlying ontologies, understand how they are constructed by actors, and propose appropriate methods of formalising and using them [17]. The research was progressively reframed to address three main questions: (1) How can ontologies support urban interoperability at all levels (both between systems and people)? (2) Which sources can be used to initiate, populate, and maintain urban ontologies? (3) What are the potential applications of ontologies in the urban domain?  
The present limitations of CityGML [18] in terms of supporting high-level interoperability between different urban applications are discussed within the context of COST C21. It has been suggested that 3D representations may play a central role in communications between decision-makers and the population when complemented with higher level urban ontologies interconnecting urban information. CityGML has been successfully integrated with OSM, a soft-mobility ontology, and with OTN, an Ontology of Transportation Networks [19], both developed in OWL. OSM can supplement the soft-mobility aspects lacking in OTN and CityGM was useful for visualisation and communication.
## 6 ML and the Security of Blockchain
### Adversarial Use of ML in Blockchain Security  
While ML offers significant benefits for enhancing blockchain security, it also introduces new vulnerabilities. Adversaries can leverage AI and ML-based systems to execute sophisticated attacks, such as 51% attacks or Sybil attacks, at scale. For instance, research [114] discusses how AI bots may be exploited to carry out these attacks, and [115] reports that an AI bot attack resulted in a $20 million loss for Ethereum. Additionally, [116] demonstrates how large language platforms can be used to identify smart contract vulnerabilities, potentially enabling malicious actors to exploit them. Despite these challenges, the rapid advancements in ML and generative AI (GenAI) provide an opportunity to design more effective systems for early detection and prevention of adversarial attacks.
# An Environmental Perception Framework for Robotic Fish Formation Based on Machine Learning Methods
## 4 The Velocity Prediction of Two Robotic Fishes
### The Phalanx Formation
#### 4.3.1 The Results Analysis  
Through analyzing the contours in pressure fields of two fishes in the phalanx formation shown in Figure 13, we found that the pressure generated by the swing of fish body will affect the adjacent fish and the flow field on both sides of the fish body are not symmetrical. When the inlet flow velocity is small, as shown in Figure 13a,c, the pressure of the body surface caused by the swing of the fish is positive or negative. When the inlet flow velocity is large, as shown in Figure 13b,d, the flow has a greater impact than the swing of fish on the pressure of the body surface, which is almost negative and the pressure at the head is obvious. When the spacing between the two fishes changes, as shown in Figure 13a-d, the pressures on the adjacent side (\(p1_{iR}\) and \(p2_{iL}\)) are affected differently by the swing of another fish.  
We chooe the data monitored by sensors \(S_{0}\), \(Si_{1 iL}\) and \(S2_{iR}(1\leq i\leq 7)\), which are located outside the phalanx formation. We calculated the mean square deviation of kinematic pressure at different spacing at a certain \(U\), as shown in Table 8. Through analyzing the data monitored by sensors, we found that the kinematic pressures at head are almost unaffected by spacing, as shown in Figure 14a,b and Table 8. Thus, \(p1_{0}\) can be used to train the predictive model of inlet flow velocity for No. 1 fish and \(p2_{0}\) can be used to train the predictive model of inlet flow velocity for No. 2 fish.  
Figure 12: The fitting image of the predictive model.  
Figure 13: _Cont._Figure 14: _Cont._  
Figure 13: The contours in pressure field of two fishes in the phalanx formation: (**a**) at the spacing of 0.2 \(L\) and \(U=0\)\(L/\)s; (**b**) at the spacing of 0.2 \(L\) and \(U=1\)\(L/\)s; (**c**) at the spacing of 1 \(L\) and \(U=0\)\(L/\)s; and (**d**) at the spacing of 1 \(L\) and \(U=1\)\(L/\)s.