esa-sceva/satcom-chunk-collection huggingface dataset:
# Bistatic Landmine and IED Detection Combining Vehicle and Drone Mounted GPR Sensors
## 1 Introduction
As shown in Figure 1, a transmitter is placed on a vehicle with the antenna looking ahead and a receiver is placed on a UAV with the antenna pointing straight down to the soil surface. A fast ray-tracing method has been developed to find feasible configurations of the system. Then, the resulting configurations were accurately analyzed with a Finite-Difference Frequency-Domain (FDFD) method. These configurations as well as the multimonostatic configuration (corresponding to just a DLGPR system on board a moving UAV) were compared by post-processing the simulated scattered field with a SAR algorithm.
## 6 Results and Discussion
### OWC Performance Evaluation
#### 6.2.1 Performance With Regular Waves  
During the simulations, two regular wave conditions were considered the first wave is weak with a wave period \(T\) = 10 s and a wave amplitude \(A\) = 0.8 m from 0 s to 22.5 s. From 22.5 s to 50 s, we considered a stronger wave with a wave period \(T\) = 10 s and a wave amplitude \(A\) = 1.3 m as shown in Figure 13.  
The flow coefficients of the OWC in the uncontrolled case and with the airflow control using the traditionally tuned PID (ZN-PID) and the optimized PID (SGRS-PID) are illustrated in Figure 14.  
The figure shows that in the uncontrolled case the flow coefficient surpasses the threshold value 0.3 which will provoke the stall effect, whereas in both controlled cases the flow coefficients were regulated below 0.3 thanks to both ZN-PID and SGHS-PID controllers. However, when zooming to the curves it is observed that the SGHS-PID manages to provide a closer flow coefficient to the threshold value than that of the ZN-PID.  
Figure 15 shows that in the uncontrolled case, the airflow speed continues to increase; however, in both controlled cases, the airflow speed decreased thanks to both ZN-PID and SGHS-PID controllers, but with a slight superiority for the SGHS-PID.  
Figure 14: Flow coefficients of the OWC in uncontrolled and controlled cases. (**a**) Flow coefficients versus time. (**b**) Zoom-in section of the flow coefficients.  
Figure 13: Considered waves as input to the wave-to-wire modelThe obtained turbine torques of the PTO in the uncontrolled case, ZN-PID controller and SGHS-PID controller are shown in Figure 16a, Figure 16c,e, respectively. Furthermore, the generated powers for the uncontrolled case, ZN-PID controller and SGHS-PID controller are shown in Figure 16b, Figure 16d,f, respectively.  
It may be observed that during the uncontrolled case the torque has been affected by the stall effect which reduced it in terms of average value to 61.16 \(N.m\). On the other hand, the airflow control manages to avoid the stall effect and increase the torques in terms of average values to 64.22 \(N.m\) and 66.71 \(N.m\) for ZN-PID and SGHS-PID, respectively. As a result from the obtained torques, the generated powers in the uncontrolled case is the lowest with \(-\)15.52 kW and the highest power is generated when using the SGHS-PID with \(-\)17.38 kW followed by 18.55 kW when using a ZN-PID in the proposed airflow control scheme.  
Figure 16: OWC torque and power outputs. (**a**) Torque in the uncontrolled case, (**b**) Power in the uncontrolled case, (**c**) Torque with ZN-PID, (**d**) Power with ZN-PID, (**e**) Torque with SGHS-PID, (**f**) Power with SGHS-PID.  
Figure 15: Airflow speed in uncontrolled and controlled cases.
# A Grey-Box Ensemble Model Exploiting Black-Box Accuracy and White-Box Intrinsic Interpretability
## 6 Experimental Results
### Grey-Box Models vs. White-Box Models  
Figures 3-8 present the performance of the Grey-Box and White-Box models. Regarding both metrics we can observe that the Grey-Box model outperformed the White-Box model for each Black-Box and White-Box classifier combination in most cases, especially on EduDataA, EduDataAB, and Australian datasets. On Coimbra dataset the two models exhibit almost similar performance, with Grey-Box model being slightly better. On Winsconsin dataset the Grey-Box model reported better performance in general, except for ratio 40 and 50 where the White-Box model outperformed the Grey-Box, utilizing RT as White-Box base learner. The interpretation of Figures 3-8 reveals that for EduDataA, EduDataAB and Bank marketing datasets, as ratio value increases, the performance of all models increases too, while on the rest datasets, it seems that we cannot state a similar conclusion. We can also observe that the Grey-Box model performs better in most cases for small ratios (10%, 20%, 30%) while it seems that for higher ratios (40%, 50%) the White-Box exhibits similar performance as the Grey-Box model. Summarizing, we point out that the proposed Grey-Box model performs better for small labeled ratios. Additionally, the Grey-Box (SMO-RT) reported the best overall performance reporting the highest classification accuracy in three out of six datasets, while the Grey-Box reported the best performance utilizing RT as White-Box learner, regarding all utilized ratios.  
Figure 4: Box-plot with performance evaluation of Grey-Box and White-Box models for EduDataAB dataset.  
Figure 5: Box-plot with performance evaluation of Grey-Box and White-Box models for Australian dataset.  
Figure 6: Box-plot with performance evaluation of Grey-Box and White-Box models for Bank dataset.
# Reflection Propagation Law of Electromagnetic Waves in U-Shaped Roadway
## 6 Analyses of Simulation Experiments
### Antenna Emission Frequency Analysis  
Figure 21 shows the field strength variation in the antenna transmitting frequencies of 0.4 GHz, 0.45 GHz, and 0.6 GHz with increasing distance from the relay node and the receiving point, respectively. Let the relative permittivity of the roadway wall be 6. The distance from the relay node to the plane \(m_{5}\) is \(w_{3}\) = 0.3 m, the distance from the receiving point to the plane \(m_{6}\) is \(w_{2}\) = 0.3 m, the height of both the relay node and the receiving point is 0.3m, and the receiving point is moved in the same way as Figure 20.  
From Figure 21, with the increase in the distance between the relay node and the receiving point, the field strength of the receiving point shows an overall trend of attenuation. The higher the frequency, the greater the overall attenuation of the field strength at the receiving point. The main reason is that the presence of corners makes electromagnetic waves with shorter wavelengths consume more energy when passing through. Moreover, the reflection phenomena in the roadway become more prominent, increasing the loss,  
Figure 21: Field strength at the receiving point as a function of the distance between the relay node and the receiving point at different frequencies.  
Figure 20: Field strength at the receiving point as a function of the distance between the relay node and the receiving point for different height differences.  
which is consistent with the conclusion of Figure 15. Therefore, when sensors are placed in the U-shaped roadway, electromechanical equipment with lower frequencies should be avoided to reduce electromagnetic interference.
## 2 Problem Statement and Model Formulation
### Integer Linear Programming Model
Constraints (4), (5), and (6) are flow balancing constraints, where (4) means all tasks start from the virtual origin node, (5) means all tasks cannot be reserved at intermediate nodes, and (6) means all tasks are transferred to the virtual termination node. Constraint (7) limits the maximum transmission time of tasks. Constraints (8) and (9) represent the capacity limits of nodes and links in the network. Constraint (10) denotes the duration of a task that is less than {Blo}_d on the same satellite. Constraint (11) indicates that links with insufficient capacity cannot be defined as transmission routes. The decision variables are defined in Constraint (12).
## 4 Design Analysis  
### Ballistic Number  
JAXA requires the satellite to have a maximum ballistic number of 120 kg/m\({}^{2}\)[3]. As seen in the calculations below, a maximum mass of 5.225 kg, with a safety margin, conforms to JAXA's requirements. A detailed breakdown in Section 4.2 shows the Light-1 assembly weighs 5.040 kg and hence satisfies the requirements.  
\[BN=M/(C_{d}\times A)=118.6\ kg/m^{2}, \tag{1}\]  
was used to calculate the ballistic number where  
M: the mass of a satellite [kg];  
\(C_{d}\): coefficient of drag (=2) [*];  
A: minimum average frontal area [\(m^{2}\)].  
A value of 2 was used for the coefficient of drag as it was specified by the satellite launchers. An average of the horizontal (0.1 \(\times\) 0.1 = 0.01 m\({}^{2}\)) and vertical (0.1 \(\times\) 0.3405 = 0.03405 m\({}^{2}\)) area was used to acquire the minimum average frontal area (0.022 m\({}^{2}\)).
## 3 Results
### Aspects of Interest  
We are particularly interested in some key elements of Sybil attack resistance schemes. First, what level of Sybil attack resistance they provide when deployed (see Table 4). We assume that the mechanism in question is deployed to a _large-scale_ system, as many Sybil attack resistance effects only materialise with high usage volume, as can be seen when considering the likelihood of 51% attacks on PoW systems with low popularity [38]. For Sybil attack resistance, we introduce three categories: Strong Sybil attack resistance; meaning a mechanism that, when applied to a sizeable system, could not be attacked, even by an irrational attacker, unless they incur tremendous cost. Limited Sybil attack resistance; meaning an attacker might be successful with an attack if they are ready to incur significant cost and orchestrate an attack over a long time. No Sybil attack resistance, meaning an attacker could easily overwhelm a public/permissionless system using the mechanism in question. The latter category is not to be seen as criticism of the mechanism: it mostly applies to environments in which the mechanism designers do not have to consider Sybil attacks.  
Second, whether an incentive scheme is part of the proposed mechanism. Here, we analyse whether participants receive rewards for adherence to the protocol and/or punishment for deviating from it. Third, the application of random/pseudorandom numbers during the execution of the mechanism. Fourth, whether the mechanism is somehow linked to the physical world, e.g., by asserting on messages generated by users having access to a central processing unit (CPU) with a Trusted Execution Environment (TEE). Fifth, whether a reputation system is applied in the context of the mechanism: such a system may be used to inform leader selection based on reputation signals from other participants or from central sources. Sixth, whether the mechanism is intended to be applied in permissioned or permissionless settings. Here we are particularly interested in outlier mechanisms with strong Sybil attack resistance that are applied in permissioned settings or mechanisms with weak Sybil attack resistance being applied in permissionless settings. Last, we outline the leader selection method, foremost by analysing whether the mechanism relies on (pseudo-)randomness, an election, or a pre-formed committee. Many mechanisms combine multiple factors. In such case, we aim to call out the dominant factor.
## 2 Sensing and Actuation Principle
### Differential Capacitance Sensing  
Capacitance displacement sensing is a technology that detects small displacements by monitoring the capacitance changes in a variable-gap parallel-plate capacitor. The basic structures are shown in Figure 2.  
In the initial state [36], as shown in Figure 2a, the capacitance value C\({}_{0}\) is:  
\[C_{0}=\frac{\epsilon_{0}\epsilon_{\mathrm{r}}\mathrm{A}}{\mathrm{d}_{0}} \tag{1}\]  
where d\({}_{0}\) is the initial distance between the TM and the electrode, A is the area of the electrode, \(\epsilon_{\mathrm{r}}\) is the relative permittivity which is identical to 1 in vacuum conditions, and \(\epsilon_{0}\) is the vacuum permittivity. When the TM moves, the gaps between the TM and the electrode will change, which causes the capacitance of the parallel-plate capacitor to vary.  
If the displacement is small enough, the relationship between the displacement and the capacitance change can be written as:  
\[\frac{\mathrm{C}}{C_{0}}\approx\frac{\Delta\mathrm{d}}{\mathrm{d}_{0}} \tag{2}\]  
In order to further improve the sensitivity of the measurement, a differential capacitive design (Figure 2c) is applied, in which the upper and lower electrodes are fixed, whereas the TM is movable.  
Figure 1: Schematic structure of the inertial sensor (IS) system.  
Figure 2: Schematic structures of variable capacitances (**a**) Single capacitor; (**b**) The gap change for single capacitor; (**c**) The gap change for differential capacitor.  
After using the same method as above, the relationship between the displacement and the capacitance change is:  
\[\frac{C}{C_{0}}\approx 2\frac{\Delta\mathrm{d}}{\mathrm{d}_{0}} \tag{3}\]
# Power Quality Enhancement in a Grid-Integrated Photovoltaic System Using Hybrid Techniques
## 6 Proposed Method
### Binary Particle Swarm Optimization  
Initially, the global MPPT technique was carried out by using the BPSO method, in order to overcome mismatching phenomena problems related to partial shading. The BPSO-based MPPT technique performance was always able to reach the GMPP, allowing the increase in the PV system efficiency. The input for the proposed BPSO calculates the PV power and its rate of conversion, where the change in reference current is referred to as the output. The GWO algorithm is used in the implementation of the MPPT method [19]. BPSO is the combination utilized to monitor the global MPP (GMPP) of the PV. The BPSO technique is used to attain the maximum global point, which exploits the maximum power extracted in the PV arrangements. In BPSO, the bit string and velocity of the particle are created in the range of [0, 1]. The velocity of the particle is defined as the bit possibility to obtain a value of 1. The BPSO is considered an adequate algorithm to enhance the efficiency of the distribution system.  
\(X^{i}{}_{k}\) and \(V^{i}{}_{k}\) represent the particle's position and the velocity at the \(k^{th}\) iteration, respectively. The \(i^{th}\) particle's velocity at the iteration \(k+1\) is calculated using Equation (8).  
\[V^{i}{}_{k+1}\!\!=\omega.V^{i}{}_{k}+\text{C1.}R1\;(P_{best}-X^{i}{}_{k})+ \text{C2.}R2\;(G_{best}-X^{i}{}_{k}) \tag{8}\]  
where the random values are represented as \(R1\) and \(R2\), and the inertia weight factor is represented as \(\omega\), which is expressed in Equation (9).  
\[\omega=\omega_{max}-\{(\omega_{max}-\omega_{min})-\;\text{k}_{max})\}\times k \tag{9}\]  
where the maximum amount of iteration considered for BPSO is \(\text{k}_{max}\). Moreover, the particle's position is updated by adding the previous position and current velocity value, which is shown in Equation (10).  
\[X^{i}{}_{k+1}=X^{i}{}_{k}+V^{i}{}_{k+1} \tag{10}\]  
The swarm expression of particles is retained, which is unchanged in BPSO. The logistic transformation \(S(V^{i}{}_{k})\) is utilized to accomplish the modification, as shown in Equations (11) and (12).  
\[S(V^{i}{}_{k+1})=sig\;mod\;e\Big{(}V^{i}{}_{k+1}\Big{)}=\frac{1}{1+\exp(V^{i }{}_{k+1})} \tag{11}\]  
\[\text{If rand }<S(V^{i}{}_{k+1})\;\text{then}:\;X^{i}{}_{k+1}=1; \tag{12}\]  
\[\text{Else }:\;X^{i}{}_{k+1}=0;\]  
where the sigmoid limiting transformation is represented as \(S(V^{i}{}_{k})\) and the quasi-random value selected among \([0,\,1]\) is represented as \(rand\). Figure 4 shows the flowchart for the proposed BPSO-GWO method.
# Enhancing Street-Crossing Safety for Visually Impaired Pedestrians with Haptic and Visual Feedback
###### Abstract  
Safe street crossing poses significant challenges for visually impaired pedestrians, who must rely on non-visual cues to assess crossing safety. Conventional assistive technologies often fail to provide real-time, actionable information about oncoming traffic, making independent navigation difficult, particularly in uncontrolled or vehicle-based crossing scenarios. To address these challenges, we designed and evaluated two assistive systems utilizing haptic and visual feedback, tailored for traffic signal-controlled intersections and vehicle-based crossings. The results indicate that visual feedback significantly improved decision efficiency at signalized intersections, enabling users to make faster decisions, regardless of their confidence levels. However, in vehicle-based crossings, where real-time hazard assessment is crucial, haptic feedback proved more effective, enhancing decision efficiency by enabling quicker and more intuitive judgments about approaching vehicles. Moreover, users generally preferred haptic feedback in both scenarios, citing its comfort and intuitiveness. These findings highlight the distinct challenges posed by different street-crossing environments and confirm the value of multimodal feedback systems in supporting visually impaired pedestrians. Our study provides important design insights for developing effective assistive technologies that enhance pedestrian safety and independence across varied urban settings.  
haptic feedback; visual feedback; visually impaired pedestrians; traffic safety; crossing decision Article
# Attack Categorisation for IoT Applications in Critical Infrastructures, a Survey
## 2 Background
### Wireless Communications  
Securing CIs is an important task, even more so with the evolution and incorporation of wireless communications into increasing devices and equipment on a larger scale and the inclusion of IoT. The IoT requires new methods for attack categorizations compared to Wireless Sensor Networks (WSN) and Mobile Ad-hoc NETworks (MANET) since these objects are known as very weak. They are limited in computing capacities, which prevents them from embedding very secured code and relied on limited power sources prone to attacks leading to an energy drains and a stop of the service these IoT devices are expected to deliver.  
With this addition, these devices can join the plethora of other types of CI equipment that are interconnected with each other through the Internet However, although network access grants the possibility for attackers to access previously inaccessible targets, the use of the wireless medium also provides other issues. Although many different wireless protocols exist for various types of uses, the most common and even mainstream technologies, such as Wi-Fi and Bluetooth, all use the same portion of the radio spectrum, reserved internationally for Industrial, Scientific, and Medical (ISM) purposes.  
Since the ISM band is free access, it is, therefore, shared with a multitude of different devices, from home network devices to microwaves. As such, all data transiting through this public domain is susceptible of being captured, analysed, or even exploited. Furthermore, unlike wired networks where direct access to the infrastructure is required, attacks can exploit the wireless radio range to interact with the target network.  
Protecting and securing wireless communications is an ongoing challenge, since the medium is both shared and inherently unprotected. Many solutions exist to protect the exchange of data, such as the common security protocols Wired Equivalent Privacy (WEP) and Wi-Fi Protected Access II (WPA2). However, these systems are not infallible and, when broken, lose their usefulness.  
This is even more significant when noting the widespread use of WPA2, which, after 14 years of certification, was broken in 2017 [6]. In many cases, these protocols are not used as their many uses are towards Infrastructure-centric networks, revolving around a single network access point. In point-to-point ad-hoc networks, however, each device establishes its own links with its neighbours. This means that each participant must be capable of securing all communications between themselves and the interlocutor. Securing these exchanges, as well as rendering the everyday wireless network usage more robust is also an ongoing challenge.
# Intelligent Detection and Segmentation of Space-Borne SAR Radio Frequency Interference
## 4 Methodology
### Evaluation Measures  
To appraise the SISNet, the performance of different semantic segmentation algorithms is evaluated qualitatively and quantitatively. Qualitatively evaluating the algorithms, we compare the visual divergence of image segmentation performance by deploying different segmentation methods. Next, we quantitatively examine the mean intersection over union (mIoU), accuracy, precision, and F1 score performance of the test dataset. The expressions of these four indicators are as follows:  
\[IoU=\frac{TP}{TP+FP+FN}, \tag{10}\]  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN}, \tag{11}\]  
\[Precision=\frac{TP}{TP+FP}, \tag{12}\]  
\[F1=\frac{2\times Precision\times Recall}{Precision+Recall}, \tag{13}\]  
where \(TP\) stands for the number of properly detected interference pixels, \(TN\) stands for the number of properly detected background pixels, \(FP\) is the number of falsely detected interference pixels, \(FN\) is the number of falsely detected background pixels, and recall represents the recall rate, the formula for which is:  
\[Recall=\frac{TP}{TP+FN}, \tag{14}\]  
Figure 15: The network structure of proposed SISNet.
# Data-Driven Modular Vehicle Scheduling in Scenic Areas
## 4 Experimental Results and Analysis
### Basic Optimization Results of M2  
Figure 5 shows the variation in cost savings of M2 compared to M1 across 30 simulation experiments. The results indicate that M2 achieves a degree of cost savings in each simulation. In most cases, M2 saves more than USD 40 compared to M1, with maximumsavings exceeding USD 100. Overall, M2 demonstrates a more substantial cost-saving effect than M1, offering considerable financial benefits for tourist attractions.  
Additionally, vehicle utilization is a critical factor in evaluating the effectiveness of scenic transport. Higher vehicle utilization allows for a more efficient deployment of resources and reduces unnecessary overhead costs. Figure 6 presents the difference in vehicle utilization between M2 and M1 across 30 simulations.  
Figure 5: The total cost savings of M2 compared to M1.  
Figure 6: Difference in vehicle utilization rates between M2 and M1.  
As shown in Figure 6, the vehicle utilization rate of M2 consistently exceeds that of M1 in each simulation, indicating that M2 better leverages the modular vehicle's flexible capacity adjustments to more effectively align supply with demand than M1. Furthermore, comparing Figures 5 and 6, the trend in vehicle utilization closely mirrors that of total cost savings, suggesting a positive correlation between vehicle utilization and cost reduction. This implies that higher vehicle utilization contributes to lower operating costs. Therefore, scenic area managers might consider optimizing vehicle utilization by strategically using modular vehicles' capacity flexibility to achieve overall cost savings.