esa-sceva/satcom-chunk-collection huggingface dataset:
# Challenges, Threats, Security Issues and New Trends of Underwater Wireless Sensor Networks
## 3 Threats and Challenges in Underwater Wireless Sensor Networks (UWSNs)
### Selective Forwarding Attack  
In this attack, malicious nodes behave like legitimate nodes, but selectively drop packets, and refuse to forward the received packets. However, there is a risk that the neighbor node may find other paths to route the packet to the destination node. Hence, in order to avoid being detected it selectively forwards certain packets and drops certain packets. The attacker interested in suppressing and modifying a packet originating from few selected nodes can reliably forward the remaining and limit suspicion of its wrongdoing [30].  
To detect a selective forwarding attack, pre-defined watchdog mechanism can be used for WSN, which have pre-defined rules for raising intrusion alerts. In this mechanism, attacks can be detected by allowing nodes to listen to the next hop nodes in a broadcasting transmission [31]. Unfortunately, the pre-defined watchdog mechanism is unsuitable for UWSN. This mechanism will fail in the case of a collision due to uncertainty ambiguity in the receiver, inadequate transmission power, fake misbehavior, and partial packet dropping. In [32], evidential evaluation is used to identify the selective forwarding attacks, which utilize the Dempster-Shafer theory of combined multiple evidences. Hop-by-hop cooperative detection scheme was proposed in [33] to detect and mitigate the selective forwarding misbehaviors for WSN. The trust management and reputation mechanisms based on behavior evaluation can be used to detect these attackers and then isolate them from the network [34; 35].
## 3 Bearing Estimation Methods  
When an acoustic wave is incident on a planar water/ice interface, ice seismic waves propagate in three fundamental models, the \(L_{P}\) wave (longitudinal plate wave), \(S_{H}\) wave (horizontally polarized shear wave) and \(F_{L}\) wave (flexural wave), if the frequencies are low and the wavelengths are greater than the ice thickness [16; 17; 18; 19; 23]. The \(L_{P}\) wave and \(F_{L}\) wave are composed of the \(P\) wave (compressional wave) and \(S_{V}\) wave (vertically polarized shear wave), i.e., \(P\)-\(S_{V}\) wave. The particle motion of the \(L_{P}\) wave and \(F_{L}\) wave mainly exists in the vertical radial plane parallel to the incident wave, while the particle motion of the \(S_{H}\) wave is perpendicular to this vertical radial plane. According to these characteristics of seismic waves in ice, a rotational analysis for source-bearing estimation based on maximizing the radial signal power has been developed [23]. Moreover, so has a straightforward and effective set of polarization filters that can be adapted to bearing estimation [28; 29]. The approaches are briefly introduced below.
# Decreasing the Rail Potential of High-Speed Railways Using Ground Wires
## 3 Rail Potential Problem in High-Speed Railways
### Possible Solutions  
According to Equation (5), to reduce the rail potential, we must take measures to decrease the rail characteristic impedance and/or to increase the coupling between the rail and OCS. This conclusion can be generalized further in order to include AT, BT and T-R+NF power supply systems. The rail can be viewed as a portion of the return circuit. The return circuit may include other conductors, such as a PW and NF. Therefore, the theoretical measures to suppress rail potentials are as follows: (1) to decrease the longitudinal serial impedance of the return circuit; (2) to increase the transversal shunt conductance of the return circuit; (3) to enhance the electromagnetic coupling between the supply circuit and the return circuit. It is clear that the existence of a PW in AT and an NF in T-R+NF feeding systems can bring the virtues of (1) and (3).  
For PDL AT supply systems, the following technical countermeasures can be taken to suppress the rail potentials:  
1. Bonding the rails of different tracks sufficiently.  
Increasing the connections between the rail and PW (called CPW in some studies).
* Grounding the PW using OCS post foundations.
* Laying one or two directly buried bare wires, which are adequately connected to the rail and PW.
* Utilizing the foundation grounding resistance of various structures along the railway.
* Constructing a special grounding pole along the railway.  
Since the rail is also used by railway signal systems, the connection and grounding of the rail must be designed carefully. Normally, planning the traction return circuit should conform to the demands of the signal system to obtain a good EMC on track. Due to the limitation of the interval of signal track circuits, an additional buried bare wire seems a promising solution.
# A Review of Recent Advances and Challenges in Grocery Label Detection and Recognition
## 6 Product Label Analysis
### Text Detection
#### 6.1.1 Regression-Based Methods  
Considering text instances as objects, regression-based methods predict candidate coordinates to define the text regions. Given the physical limitations of bounding boxes, the frameworks introduce post-processing steps to handle multi-oriented or arbitrary-shape text. The papers [109; 110] suggested the rotation of Region Proposal Network (RRPN), predicting the angle of inclination. Rotational anchor improves the performance of the model when compared with horizontal box prediction. Naturally, the additional boxes generate greater computing costs, but they remain near to the Faster R-CNN. RRD [111] learns the rotation-sensitive features to improve the results, while TextBoxes++ [112] combined with CRNN boosts the accuracy of arbitrary-oriented text detection. EAST [113] is a fast and accurate detector which predicts multi-oriented words or lines, without intermediate steps. Vertical and curved text are not detected by this method. SegLink [105] detects text segments and then links these segments to obtain the final text region. The method proposed in [114], SegLink++, was adjusted to detect dense and arbitrary-shaped scene texts with an attractive and repulsive link system. CounterNet [115] incorporated further steps to improve the arbitrary-shape text detection, emphasizing the adaptive-RPN and local orthogonal texture-aware module. [PERSON] et al. [116] joined the CNN with Graph Convolution Network (GCN) to predict small rectangular components and link to their neighbours to also handle arbitrary-shape text. A different approach was proposed in [117], where the text instances are established in the Fourier domain. MOST [118] uses a module to adjust the receptive field and the instance-wise IoU loss to deal with texts of different scales and aspect ratios.
## 5 Simulation Results
### Number of Collisions  
A metric related to the efficiency of energy consumption is the number of collisions occurring during the phase of route creation. In this sense, a lower number of collisions means a lower energy consumption and a suitable transmission range adopted. This problem is inherent to non-guided channels such as underwater channels. In the SPRINT protocol used here, there is a TDMA-based mechanism for avoiding collisions, but due to the random position of the nodes in the network, is impossible to avoid them completely in the initial creation path phase.  
The results obtained for both methods, FAHP and SPRINT-Fuzzy, are presented in Figure 10. It can be seen that FAHP is as good as SPRINT-Fuzzy, or even better in case of a  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**No. Nodes**} & \multirow{2}{*}{**Tx Range (km)**} & \multicolumn{2}{c}{**Av. No. Hops**} \\  & & **Sprint** & **FAHP (\%)** \\ \hline
100 & 4 & 10.7 & 15.57 (+46\%) \\
100 & 5 & 11.62 & 15.54 (+33.7\%) \\
100 & 6 & 11.01 & 16.49 (+49\%) \\
100 & 7 & 13.37 & 12.94 (\(-\)3.2\%) \\
400 & 2 & 12.28 & 16.41 (+33.6\%) \\
400 & 3 & 11.87 & 19.3 (+62.6\%) \\
400 & 4 & 11.73 & 15.83 (+35\%) \\ \hline \hline \end{tabular}
\end{table}
Table 10: Statistical comparison for average No. hops.  
Figure 9: Average number of hops (left bar: SPRINT with fuzzy logic, right bar: FAHP method). Number of nodes: (**a**) 100, (**b**) 400.  
high number of nodes (e.g., 400 nodes), which is a more complex network and has more free degrees to make routes.  
As previously, Table 11 is introduced, presenting the values obtained in the bar graph of Figure 10.
# Monitoring of Structures and Infrastructures by Low-Cost GNSS Receivers
## 3 The Experience
### The Network and the Installation on the Bridge  
The local monitoring network is composed of only three stations (called C1, C2 and C3) placed in such a way as to have information on the longitudinal deformation (bases C1-C3 and C2-C3) as well as on the relative transversal motion between a pier and a deck. The position of the points is illustrated in Figure 4. The three stations started operating in February 2016. As a matter of fact, the design was enriched with three additional stations located on the central pier in 2020; however, we will examine only the smaller network, for which a full data set that is about 4 years long is available.  
As can be seen in Figure 4, the mounting of the antennas brings them above the level of the bridge in order to guarantee better sky visibility.  
This small local network has been analyzed locally to display its internal differential motion. C3 has been chosen as the master station, and the origin of a Cartesian system has been fixed there; however, any of the three stations may be used as a local master. The \(X\)-axis is directed along the bridge, the \(Y\)-axis is transversal, and the \(Z\)-axis is vertical. Beyond these three points, the monitoring network can be considered to include another permanent geodetic station \(S\), part of the geodetic Italian network, 2 km from the bridge. The use of \(S\) as reference, although providing noisier positions, is quite useful for finding long-term motions of the bridge with respect to its environment, as we will see in Section 3.6.
###### Abstract  
This paper aims to achieve a large bridge structural health monitoring (SHM) efficiently, economically, credibly, and holographically through noncontact remote sensing (NRS). For these purposes, the author proposes a NRS method for collecting the holographic geometric deformation of test bridge, using static image sequences. Specifically, a uniaxial automatic cruise acquisition device was designed to collect static images on bridge elevation under different damage conditions. Considering the strong spatiotemporal correlations of the sequence data, the relationships between six fixed fields of view were identified through the SIFT algorithm. On this basis, the deformation of the bridge structure was obtained by tracking a virtual target using the optical flow algorithm. Finally, the global holographic deformation of the test bridge was derived. The research results show that: The output data of our NRS method are basically consistent with the finite-element prediction (maximum error: 11.11%) and dial gauge measurement (maximum error: 12.12%); the NRS method is highly sensitive to the actual deformation of the bridge structure under different damage conditions, and can capture the deformation in a continuous and accurate manner. The research findings lay a solid basis for structure state interpretation and intelligent damage identification.  
noncontact remote sensing (NRS); optical flow algorithm; structural health monitoring (SHM); uniaxial automatic cruise acquisition device
# Intelligent Transmission Control Scheme for 5G mmWave Networks Employing Hybrid Beamforming
## 5 Experimental Results
### Performance Comparison with Existing Methods
#### 5.2.6 Beamforming Accuracy in Dynamic User Environments  
Beamforming accuracy may decrease when more users join because of additional interference. However, even with an increasing number of users, the 5G mmWave framework's superior control algorithms can retain high accuracy.  
Beamforming accuracy in a millimeter-wave system is the accuracy with which a beamforming system aligns its broadcast or received beams toward the desired user directions. It tests the system's capacity to lower interference, increase signal strength, and change with the channel conditions.  
Beamforming accuracy metric: Accuracy is measured by the following normalization formula: \(Beamforming\ accuracy(\%)=1-\frac{\left\|\hat{\
abla}_{ideal}-\hat{\
abla}_{estimated} \right\|}{\|\hat{\
abla}_{ideal}\|}\).  
Figure 8 and Table 10 indicate the outcomes of beamforming accuracy, and the suggested technique outperforms SIM-ADMT at 15%, Deep IA at 20%, and UL-SCH + PUSCH at 40%, with a high accuracy of 70% at 20 users. The suggested method improves even more to 75%, as user numbers reach 40, whereas SIM-ADMT is at 25%, deep IA rises to 30%, and UL-SCH + PUSCH reaches 45%. The suggested system maintains a reasonable accuracy of 68% at 60 users, while SIM-ADMT achieves 35%, FB-TCP + MP-TCP + Deep IA achieves 40%, and UL-SCH + PUSCH achieves 50% of the accuracy. The suggested technique outperforms SIM-ADMT at 65%, Deep IA at 70%, and UL-SCH + PUSCH at 80%, with an amazing 98% accuracy by 100 users. With all things considered, the suggested approach continuously exhibits higher beamforming accuracy, proving its usefulness in handling situations with a large user density. Compared with the proposed and existing methods, the proposed methods have a greater efficiency.  
Figure 8: Number of users vs. beamforming accuracy.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **No of User** \\ **(x-Axis)** \\ \end{tabular} } & \multicolumn{4}{c}{**SNR (dB)-(\(y\)-Axis)**} \\ \cline{2-5}  & **SIM-ADMT** & **FB-TCP** & **UL-SCH + PUSCH** & **Proposed** \\ \hline
20 & 6.3 & 6.0 & 5.0 & 8.3 \\ \hline
40 & 5.4 & 5.0 & 6.0 & 7.0 \\ \hline
60 & 4.0 & 4.5 & 3.7 & 6.6 \\ \hline
80 & 6.3 & 7.5 & 5.8 & 8.8 \\ \hline
100 & 7.5 & 8.0 & 7.0 & 9.0 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Numerical outcomes of SNR (dB).
# Dynamic Decoupling and Trajectory Tracking for Automated Vehicles Based on the Inverse System
## 4 Simulation Results and Analysis
### Verification of Tracking Performance
#### 4.2.1 Scenario 1: Lane Change with Deceleration on Dry Road Surface  
In this scenario, the vehicle drives at the initial speed of 90 km/h on a straight and dry road with the road adhesion coefficient being 0.8, then the decelerating lane change is completed within 58 m, which is a classic collision avoidance scenario.  
Based on the proposed method, the desired vehicle motion states \(\mathbf{U}_{ref}=\left[a_{x}(t),a_{y}(t),\tilde{\varphi}(t)\right]^{T}\) were calculated and the vehicle dynamic states tracking results are shown as Figure 6.  
Figure 6 shows that the longitudinal acceleration, lateral acceleration of the vehicle coincide with the desired value and the change in each direction does not affect the other directions, indicating that the proposed method has decoupled dynamics and the vehicle dynamic states can be accurately tracked in close loop.  
As shown in Figure 7, the real vehicle velocity and lateral position accurately follow the references. The velocity tracking error of the proposed method is smallest compared with pure-pursuit control and MPC. With the same longitudinal controller, MPC and pure-pursuit control still contribute to different speed errors, which means the longitudinal control is affected by control in other directions. Even though the lateral control value was correctly calculated by MPC, the longitudinal tracking error results in a delay in the lateral tracking owning to the lateral reference related to the time and does not consider the longitudinal error within a planning cycle, which is another deterioration of tracking accuracy caused by dynamic coupling.  
To quantitatively evaluate the tracking performance of the three methods, the results were statistically analyzed.  
The statistical values of lateral error are shown in Table 1. Compared with pure-pursuit and MPC, the mean square error of velocity (\(MSE_{V}\)) of the proposed method is the smallest, and its mean square error of lateral tracking (\(MSE_{Y}\)) and yaw tracking (\(MSE_{yaw}\)) rank in the middle.  
Figure 6: The vehicle dynamic states tracking results in scenario 1.
# Design of a Chaotic Trajectory Generator Algorithm for Mobile Robots
## 5 Results
### Coverage of the Work Area  
In order to estimate the work area covered by the chaotic trajectories imposed by Algorithm 1 on the mobile robot, it was divided into a grid of 12 \(\times\) 20 (240) frames with lateral measurements of 76.66 mm; this is to have a more precise idea of the effective coverage of the differential drive mobile robot employing the proposed algorithm for exploration or search applications. Figure 10 shows the percentage coverage of the test  
Figure 8: Mobile robot’s trajectories with initial conditions \((x_{0},y_{0})=(0,0)\): **(a)** 200 iterations; **(b)** 300 iterations.  
Figure 9: Mobile robot’s trajectories for different initial conditions with 200 iterations: **(a)**\(x_{0}=0.01\), \(y_{0}=0\); **(b)**\(x_{0}=-350\), \(y_{0}=500\).  
area by the differential drive mobile robot for 100 and 300 iterations of the trajectories imposed by Algorithm 1, when the initial conditions were \((x_{0},y_{0},\theta_{0})=(0,0,0)\). We can see that with just 100 iterations the robot patrolled almost half of the work area and with 300 iterations it covered almost the entire work surface.  
Repeating the previous process 1000 times for arbitrarily selected initial conditions, using 300 iterations, an average of 84.1270% of covered work area is obtained.
## 7 Conclusions  
The introduction of a few percent of Zn(II) in perovskites yields a smoother and more dense film with fewer pinholes. Zinc fully incorporates in the structure by occupying Pb(II) sites and due to its stronger chemical interaction and smaller ionic radius the unit cell is slightly contracted, which results in enhanced nucleation and crystallization.  
The zinc-doped perovskite material has the characteristics of a liquid-like alloy, a material that behaves like a solid solution, an even dispersion of the different components throughout the lattice. The optimal percentage of Zn(II) is most likely to be between 2 and 6%, with 2.5% as anticipated optimum, especially for FA/MA materials [16; 24]. The optimum content of Zn(II) may well depend on the exact composition of the perovskite as well as the additives used in the procedure. Even a material with 20% Zn content has been reported [30] for a MAPbI\({}_{3}\)-based material without any bromide content or chloride additives.  
The zinc-doped films have bigger grains, a lower grain boundary density and a lower defect density. All these aspects contribute to an enhanced stability because decomposition tends to take place at film boundaries which are present at grain boundaries and pinholes. Another advantage of Zn's different ionic radius is that it allows for the tolerance factor to be fine-tuned towards 1. The enhanced electronic properties are also a result of the lower defect density and the bigger grains in the Zn-doped perovskite material. Non-radiative recombination takes place at these edges and a smoother surface is in all cases beneficial for device performance.  
Altogether, multiple research groups have reported several coherent interesting findings as a result of Zn(II) incorporation into perovskite solar cell materials and devices. In particular, the enhanced stability and enhanced properties obtained with Zn(II) combined with additives could have great technological relevance.  
Conceptualization, R.M.W.; writing--original draft preparation, draft based on literature thesis within the MSc program Molecular Sciences at UvA, A.K.; writing--review and editing, R.M.W. and A.K.; visualization (Figure 4), new data in Table 1, L.A.M.; supervision, R.M.W.  
This research received no external funding.  
We thank the Erasmus+ exchange program for supporting L.A.M. We thank [PERSON] for her contribution to this work and [PERSON] for graphical design in Figure 3. The authors are grateful to the UvA (Universiteit van Amsterdam) for the structural support.  
The authors declare no conflicts of interest.
# Systematic Review of Discrete Event Simulation in Healthcare and Statistics Distributions
## 4 Discussion
### Analysis of Statistical Distributions
#### 4.4.1 Patient Arrival Patterns  
Patient arrival patterns vary significantly between scheduled and unscheduled care settings. In emergency departments, the Poisson distribution is widely used to model patient arrivals, particularly in high-volume settings. This aligns with [PERSON] and [PERSON] [48], who demonstrated that Poisson distributions effectively capture the randomness of emergency arrivals while ensuring computational efficiency.  
In outpatient settings, [PERSON] and [PERSON] [49] found that a Normal distribution more accurately represents scheduled patient arrivals, especially when accounting for variations in punctuality. Their study showed that combining Normal distributions for scheduled patients with exponential distributions for walk-ins improved model accuracy by 18% compared to using a single distribution.  
While certain distributions are commonly applied, their suitability must be empirically validated for each specific healthcare setting. [PERSON] and [PERSON] [47] emphasize the importance of statistical testing and real-world data analysis to ensure that the chosen distribution accurately reflects the observed arrival patterns, optimizing model reliability.
## 3 Comparison Study on Experimental and Numerical Results  
### Laboratory Experiment  
The uniaxial compression experimental study on marble specimens [52] is used here for the calibration of the numerical model and the comparison analyses. In the laboratory experiment, the marble specimen has the size of 50 mm \(\times\) 50 mm \(\times\) 100 mm, and the main minerals are dolomite (96.98%) and calcite (3.02%). The uniaxial compression tests were conducted using an electric servo-hydraulic material testing machine (INSTRON 1346) with a constant strain rate of 4 \(\times\) 10\({}^{-5}\)/s. Complete stress-strain curve has been obtained (Figure 1), and it is shown that the uniaxial compressive strength (UCS) is 124 MPa (\(\sigma_{\rm c}\) = 124 MPa) and the elastic modulus is 23 GPa. During the experimental process, an uncooled infrared thermographic camera (SC7000 FLIR) with the temperature sensitivity of 0.01 \({}^{\circ}\)C was used to monitor the variation of rock temperature by capturing infrared radiation (IRR) images at a 100 Hz frame rate (Figure 2). The images show that the temperature accumulates on the surface of the specimen during the pre-peak stage, while the macro fractures lead to a more heterogeneously distributed temperature in the post-peak stage.  
Figure 1: A complete stress-strain curve of the marble specimen under uniaxial compression experiment [based on [52]].