esa-sceva/satcom-chunk-collection huggingface dataset:
# Seventy-Five Years since the Point-Contact Transistor: Germanium Revisited
## 4 Defect Processes in Ge  
### Intrinsic Point Defects  
The first defects that need to be considered are the intrinsic defects, i.e., self-interstitial and vacancies. These defects always exist in semiconductors for thermodynamic reasons, and their concentration in equilibrium conditions is dependent upon their formation energy. Typically, the formation energies of self-interstitial and vacancies in semiconducting materials are high, and therefore their concentrations are not significant even at higher temperatures. Nevertheless, during growth (the Czochralski crystal growth process), dopant incorporation (ion implantation) and processing there is a significantly higher concentration of intrinsic defects that forms in the lattice. This non-equilibrium concentration of intrinsic point defects is typically orders of magnitude higher than the one expected under equilibrium conditions. The concentration and properties of these intrinsic defects is important as they are the vehicles of self- and dopant diffusion, and consequently their control is key in order to be able determine the electrical activation of dopants and the dopant profile in \(n\)- or \(p\)-type regions [11; 12; 27; 29].  
Although Ge and Si are isostructural, they have a number of key differences. As was mentioned above, the main reason for the community to revisit Ge is the higher mobility of electrons and holes (although the electron mobility in some III-V compounds such as GaAs is even higher). Considering intrinsic point defects, the vacancy in Ge is the key defect, and this is not the case in Si [11; 27; 42; 43]. This has a profound impact on the diffusion and deactivation of donor atoms, as will become evident in the coming sub-section.  
Studies on point defects and self-diffusion in Ge were performed more than 65 years ago (for example [76; 77]), however, it was the seminal study by [PERSON] et al. [11] that determined that the vacancies in Ge are the energetically favourable defect with concentrations that were more significant than in Si under equilibrium conditions. This study was thereafter confirmed by density functional theory (DFT) calculations that predicted that the formation energy of the vacancies is lower than the formation energy of the self-interstitial in Ge [11; 27; 42; 43]. Further evidence to support the prevalence of the vacancy self-diffusion mechanism was derived from experiments concerning copper diffusion in dislocation-free germanium [78; 79; 80]. Concerning that the self-interstitial in Ge under equilibrium conditions is never of importance and does not practically contribute to self-diffusion (refer to [59] and the references therein), the charge state of the vacancy in Ge under \(n\)-type doping conditions was determined to be doubly negatively charged [38; 44]. Figure 1 is the Arrhenius plot comparing self-diffusion with dopant diffusion in Ge.  
Figure 1: Arrhenius plot for the diffusion coefficients of self-diffusion, \(n\)-type dopants (P, As, Sb) and \(p\)-type dopants (B, Al, Ga, In) in Ge [59].
# Wideband Circularly Polarized Antenna Based on a Non-Uniform Metasurface
## 5 Conclusions  
A non-uniform MS-based wideband CP antenna has been presented and investigated in this paper. This paper utilizes a non-uniform MS, which has the potential to not only increase the impedance BW but also to produce two additional CP bands to significantly widen the overall 3 dB AR BW of the proposed design. The measurements implemented on a fabricated antenna demonstrate that a wide operating BW of 30% (5.1-6.9 GHz) can be attained. Additionally, the antenna also exhibits good radiation characteristics and a gain in the broadside direction better than 5.6 dBic. The proposed antenna can be a potential candidate for communication systems in the C-band, such as wireless local area networks (WLANs), satellites, cordless telephones and so on.  
The presented work was carried out with the collaboration of all authors. [PERSON] was in charge of the simulations. [PERSON] and [PERSON] participated in the conception, analysis and fabrication as well as measurement. [PERSON] wrote the paper, which was then edited by all co-authors. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Antennas** & **Overall Size (\(\lambda_{o}\))** & **CP BW (\%)** & **Gain (dBic)** \\ \hline Ref. [5] & \(1.04\times 1.04\times 0.13\) & \(17.1\) & \(9.5\) \\ \hline Ref. [8] & \(0.80\times 0.80\times 0.09\) & \(17.9\) & \(8.7\) \\ \hline Ref. [9] & \(1.07\times 0.87\times 0.11\) & \(18.0\) & \(7.5\) \\ \hline Ref. [11] & \(0.81\times 0.81\times 0.09\) & \(20.3\) & \(8.6\) \\ \hline Ref. [12] & \(1.14\times 1.14\times 0.13\) & \(23.9\) & \(8.5\) \\ \hline Ref. [13] & \(0.63\times 0.63\times 0.14\) & \(16.9\) & \(7.4\) \\ \hline Ref. [15] & \(0.32\times 0.32\times 0.01\) & \(15.8\) & N/G \\ \hline Ref. [16] & \(0.86\times 0.86\times 0.04\) & \(12.8\) & \(8.4\) \\ \hline Ref. [17] & \(0.58\times 0.58\times 0.06\) & \(23.4\) & \(7.6\) \\ \hline Ref. [18] & \(0.78\times 0.80\times 0.10\) & \(20.4\) & \(6.5\) \\ \hline Ref. [19] & \(0.85\times 0.85\times 0.05\) & \(31.3\) & \(7.0\) \\ \hline Ref. [20] & \(1.18\times 0.97\times 0.07\) & \(28.9\) & \(6.8\) \\ \hline Ref. [24] & \(0.53\times 0.53\times 0.05\) & \(26.2\) & \(6.7\) \\ \hline Ref. [25] & \(0.60\times 0.60\times 0.06\) & \(19.7\) & \(6.9\) \\ \hline Proposed & \(0.80\times 0.80\times 0.06\) & \(30.0\) & \(7.6\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance comparison among the low-profile, single-fed, wideband CP antennas. BW: bandwidth.
# Quality of Service and Congestion Control in Software-Defined Networking Using Policy-Based Routing
## 7 Conclusions  
In this paper, we proposed a policy-based routing (PBR) module integrated with OSPF in an SDN environment to address the challenges of managing QoS in real-time media communications. By leveraging SDN's programmability, the PBR module dynamically reroutes traffic based on real-time network conditions on a new minimum delay path, mitigating congestion and optimizing network performance. Experimental results confirm that the proposed module reduces end-to-end delay by 80%, decreases packet loss by 73%, and improves jitter, making it an effective approach to overcoming the limitations of traditional routing protocols. The PBR module's ability to detect congestion and find alternate low-delay paths was shown to effectively address the limitations of traditional OSPF routing, which does not account for real-time network conditions.  
One potential limitation of this work is that it does not provide a mechanism for real-time applications to communicate their latency requirements to the SDN controller, which we plan to include in our future work. Our future work could explore further optimizations to the PBR module, such as integrating machine learning algorithms to predict congestion and enhance routing decisions.  
Conceptualization, I.A. and S.H.; methodology, I.A.; software, I.A.; validation, I.A., S.H. and T.C.; formal analysis, I.A.; investigation, I.A.; resources, S.H. and T.C.; data curation, I.A.; writing--original draft preparation, I.A.; writing--review and editing, I.A. and S.H.; visualization, I.A.; supervision, S.H. and T.C.; project administration, S.H.; funding acquisition, S.H. and T.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) [2021-0-00715, Development of End-to-End Ultra-high Precision Network Technologies].  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{table}
\begin{tabular}{l c c c} \hline
**Queue** & **Delay** & **Packet Loss (\%)** & **E-to-E Delay (ms)** & **Flow Completion Time (sec)** \\
**Threshold (ms)** & & & \\ \hline
150 & 5.1 & 235 & 112.7 \\ \hline
100 & 2.3 & 210 & 110 \\ \hline
50 & 2.1 & 198 & 109 \\ \hline
10 & 2.1 & 257 & 115 \\ \hline \end{tabular}
\end{table}
Table 1: Effect of queue delay threshold on performance.  
RTCP RTP Control Protocol  
OSPF Open Shortest Path First  
RIP Routing Information Protocol
# A Fast Dynamic Dim Target Tracking Approach for UAVs Using Improved Lightweight Siamese Networks
## 2 Related Work  
The issue of dynamic dim target tracking has attracted widespread concerns among scholars and much research have been conducted. The related research results focus on the correlation filtering algorithm and deep learning algorithm.
## 1 Introduction  
The application of the global navigation satellite system (GNSS) has had considerable significance during the past two decades for positioning and navigation in terms of the autonomous ground vehicle [1; 2; 3; 4], unmanned aerial vehicle (UAV) [5; 6; 7], precision farming [8; 9; 10; 11], smart city relied on information and communication technology (ICT) [12; 13; 14] and internet of things (IoT) [15; 16; 17], as well as for the GNSS-reflectometry (GNSS-R) towards agriculture [18; 19], forest monitoring [20], wind geophysical modeling [21], sea ice remote sensing [22], and its other improved algorithms [23; 24]. The GNSS receiver plays a very significant role of being integrated with other sensors, e.g., inertial navigation system (INS) [25], odometer [26], light detection and ranging (LiDAR) or laser scanner [27], camera [28], as well as the GNSS signal is promising to be jointed with the other satellite signals, e.g., synthetic aperture radar (SAR) [29; 30], to provide the accurate and continuous absolute navigation solutions. Besides, the concept of position dilution of precision (PDOP) which is originated from the GNSS positioning technique is also exploited by [PERSON] et al. to propose an optimal approach enabling an improvement of the three-dimensional (3D) deformation retrieval accuracy with respect to the SAR signal [31].
# SDN-Based Secure Common Emergency Service for Railway and Road Co-Existence Scenarios
## 4 Implementation of the Proposed SDN-Based Service for Rail and Road Coexistence
### Security Improvement: DoS/DDoS Mitigation
Overall, the given flowchart in Figure 7 outlines a security mechanism for identifying and isolating potentially malicious nodes on a network. It leverages information about the node's IP and MAC address, its past behavior, and its messaging activity during a monitoring period to make decisions about how to handle incoming data packets. It is important to note that the specific details of this process, such as the criteria for abnormal behavior detection and the duration of the observation period, may vary depending on the specific implementation and security requirements of the network.
## 4 Trajectory Prediction
### Training and Evaluation  
According to the characteristics of RNN, the data is truncated into 10-point fragments, and then the data is packed to form data packets with a size of 100. The initial learning rate is 0.1, the learning rate per training is reduced to 98%, and the total number of training steps is 20,000. In order to determine the best hidden layer size, we start training with 512 and gradually reduce to eight. We observe the change trend of the absolute error loss function and determine the best size of the hidden layer as 32. Four sets of training sets were used to train four LSTM predictors, and predictions were made on the corresponding test sets. The results are shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c} \hline
**Tester** & **Damping Value \(\mathbf{D_{01}}\)** & **Damping Value \(\mathbf{D_{02}}\)** \\ \hline Tester 1 & Train 1, Test 1 & Train2, Test 2 \\ Tester 2 & Train 3, Test 3 & Train4, Test 4 \\ \hline \end{tabular}
\end{table}
Table 1: Training set and test set under different experimental conditions.  
It is noted that the test trajectory and the predicted trajectory coincide, which proves the performance of the predictor is good and stable. In order to quantitatively evaluate the performance of the predictor, the prediction error is defined as the distance between the predicted point and the actual point. The probability density of each group of prediction errors and the position parameter \(\mu\) and scale parameter \(\sigma\) of the normal distribution are shown in Figure 4.  
Figure 4: (**a**) Definition of prediction errors. (**b**) Probability density of prediction errors.  
Figure 3: Trajectory curve comparisons of predications and test data sets.  
The experiment results show that the average prediction error of each group is less than 1 mm. Using Predictor 1 as an example, the maximum error within 99.7% of confidence probability is 1.9 mm. The prediction error of the predictor is close to the positioning accuracy of the robot, which meets the requirements of subsequent experiments.
## 3 Network Architecture and Underwater Acoustic Channel Modeling
To ensure a realistic yet tractable simulation, we adopt moderate environmental conditions with \(s=0.5\) and \(\omega=0-3\,\)m/s. The simulations are conducted within a three-dimensional deployment area of 1.5 km\({}^{3}\). Underwater acoustic channels exhibit dynamic variations in time, space, and frequency, rendering them complex and difficult to predict. Underwater acoustic channels vary dynamically in time, space, and frequency, making them complex to model. We used the AquaSimPropagation module from Aqua-Sim-NG [27], which simulates propagation delay, multipath, path loss, noise, and Doppler effects. It includes [PERSON]'s attenuation formula and frequency-dependent absorption for realistic environmental modeling. Building on this, we incorporated ocean ambient noise to better reflect real underwater conditions. This combined modeling enables a more accurate evaluation of routing protocols in practical UWSNs.
## 3 Description of the Case Study: An Existing RC Building
### Definition of the Non-Linear Numerical Models for Fixed-Base/Base-Isolated RC Structure
In practice, the intervention may be realized by means of the removal of the load on the columns through the use of hydraulic jacks that react on a pair of clamps fixed to each column to transfer the load by friction. Then, a portion of the column is cut at the isolation level where the friction-pendulum device should be located. This procedure, which is apparently very laborious, is not significantly demanding in practice and it is not invasive. In addition, the columns of the substructure at the isolation level may be connected through beams to improve the safety and robustness [3]. The single-concave friction pendulum (FP) isolators have been modeled using the non-linear link \"Friction Pendulum\" implemented in SAP2000 [25]. The latter permits one to consider compressive behavior along the vertical direction only, whereas, is able to couple the frictional properties in the X, Y directions in plan. The model reproduces the shear behavior in line with [34] and is adapted for seismic isolator bearings [35] according to the bi-linear relationships described by [21]. The parameters that have been considered to design the retrofitting intervention ware: the radius of curvature \(R\), the stiffness values \(K_{1}\) and \(K_{2}\), and the friction coefficient \(\mu_{d}\) along the two horizontal degrees of freedom (DOFs). Regarding the vertical DOF of the isolators, a linear behavior only in compression is adopted. The choice of the radius of curvature is related the isolation period of the structure [Equation (1)]. As also introduced in Section 2, the hysteretic behavior of FPS is represented by two stiffness values: the post-elastic one \(K_{2}\) that has been calculated according to the axial load on the device and the elastic stiffness \(K_{1}\) that has been assumed according to [21]. The software platform SAP 2000 [25] implements Equation (3) [22] for the variation of sliding friction coefficient \(\mu_{d}\) as a function of the sliding velocity and time. An indicative value for the sliding friction coefficient has been evaluated considering an empiric relation [22] between \(\mu_{d}\) and the ratio \(N_{5d}/N_{Ed}\), where \(N_{5d}\) is the axial load acting on the FP device in the seismic combination of actions [2; 36] and \(N_{Ed}\) represents the maximum axial load for the adopted bearing device as provided by the producer. The parameters herein assumed to reproduce the FP isolator behavior are the following: \(R=1.5\) m, \(\mu_{slow}=1\%\), \(\mu_{fats}=3\%\), \(\alpha=30\) s/m. In this way, the isolated period is equal to 2.66 s. The stiffness of the devices has been calculated according to the vertical load acting
## 4 Discussion
Moreover, we cannot ignore the fact that the number of biopsy cores performed by targeted biopsy is significantly lower compared to a systematic biopsy (median number of cores: 2-7 vs. median number of cores: 8-15), and the detection ratio (i.e., the ratio of the detection rates obtained by targeted biopsy alone and by systematic biopsy alone) is in the superiority of targeted biopsy (1.12 (95% CI: 1.02-1.23) for ISUP grade \(\geq\) 2 PCa and 1.20 (95% CI: 1.06-1.36) for ISUP grade \(\geq\) 3 PCa) [24]. This fact is also reflected in our study. An equally important question remains to be answered as to how many targeted biopsy cores are required for the detection of clinically significant disease during transperineal MRI/US-guided prostate biopsy. Based on the literature, in men undergoing mpMRI/US-guided biopsy, the first two biopsy cores diagnose the most clinically significant cancers. However, there remains a proportion of men who would benefit from additional diagnostic cores, particularly those with foci greater than 8 mm in diameter [25]. In our trial, the second and third targeted samples detected an extra 16.49% and 28.87% of clinically significant PCa cases. These additional samples detected 45.36% of all clinically significant PCa cases, indicating that at least three targeted biopsy cores might be reasonable for patients with an ROI greater than 0.5 cm\({}^{3}\) or 8 mm in diameter. The main limitation of this study is the relatively small sample size, especially of the PI-RADS 3 group. We cannot ignore the fact that 19.78% of all cases with PCa have not been diagnosed by both types of biopsy, and we do not know how many truly false negative findings we have. To confirm this fact, a repeated biopsy will be performed for this patient in surveillance protocol.
# WLQRP: A Weighting Link-Quality-Based Routing Protocol for Underwater Sensor Networks
## 5 Simulation Results
### Metrics  
Throughout the simulations, we utilize the proposed WLQRP methodology as a basis for comparison with VBF while employing the following performance metrics:  
1. Packet loss ratio: The packet loss ratio is defined as the quotient of the number of packets received by the sink and the number of packets generated by the network.
2. Residual energy ratio: The average remaining energy of the node after the packet transmission is completed in the sensor network relative to the initial energy of the node is referred to as the energy ratio.
3. Consumed energy per packet: The definition of consumed energy per packet is the total energy consumed by the network to transmit packets divided by the number of packets received by the sink node.
4. End-to-end delay: End-to-end delay is the collective time it takes from when packets are initially sent from the source node to when they are successfully received by the sink node. This includes the time it takes for the packets to propagate, the time it takes for them to be transmitted, the time they spend being held, and the time spent on calculations.
5. Hop count: The hop count is the average hop count taken by a packet to travel from the source node to the receiving node in the shortest time in a simulation.
6. Deviation factor: The proportion of the disparity between the cumulative propagation distance and the direct distance (from source to sink node) to the direct distance from the source node to the sink node [12].  
\[Deviation\ Factor=\frac{TotalDist-StraitLineDist}{StraitLineDist}\]  
TotalDist denotes the aggregate propagation distance traversed by a successfully received packet from the source node to the sink node, while StraitLineDist represents the direct linear Euclidean distance between the source node and the sink node. The magnitude of the propagation deviation factor directly influences the extent of the incremental propagation distance. Consequently, the propagation deviation factor aptly characterizes the supplementary propagation overhead endured by the packet within the network. This additional overhead encompasses heightened latency and augmented energy consumption.
## 4 Proposed Power Consumption Modes
### Motivation of Using Three Different Modes  
The well-known advantage of sleep mode is that a BS consumes zero power while sleeping, but its disadvantage is that it takes around 40 s [14] to get back into fully operational mode. On contrary, though a BS in stand-by mode consumes some small amount of power, but it takes negligible time to wake up. Therefore we utilized both of the modes so that we can have the power saving advantage from sleep mode and reduce activation delay by utilizing stand-by mode. Figure 2 graphically depicts the time and energy required to switch between active mode and sleep mode. The energy gain for being in sleep mode equals \((\overline{\textbf{1}}_{act}-\overline{\textbf{1}}_{sp})t_{slp}\), where \(\overline{\textbf{1}}_{act}\) and \(\overline{\textbf{1}}_{slp}\) represent the power consumption in active and sleep mode, respectively, and \(t_{slp}\) is the duration of the time spent in sleep mode. It is evidence from the figure that a BS needs quite a long time to wake up from sleep mode to active mode. This wake up delay issue of sleep mode has clearly been reported in the paper [14] as in Figure 3. These results had been acquired from experiments and shows that a resource takes approximately 5 s to turn off completely (i.e., to go in sleep mode) and approximately 60 s to go on active mode and be completely operational again. That is why we need to put one of the BSs in stand-by mode so that it can be activated with minimal delay when needed. It is worth mentioning that we consider the fact that implementing three different states spend extra overhead, however we make sure that the energy consumption of the overhead is less that the energy saved by the algorithm.  
Figure 3: Time measurement for switching between sleep mode and active mode (source: [14]).  
Figure 2: Graphical representation of energy saving and energy wasting due to activating BS from sleep mode.