esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Measurement and Result Analysis  
Once the S-parameters of the proposed unit cell and array were simulated using a simulation tool, such as CSTMW studio, the measurement setup for the prototype is done by placing the unit cell or meta surface array between the waveguide ports of a VNA, which is as shown in Figure 6. From the numeric simulation results, finally a 2 \(\times\) 2 sized array is chosen and fabricated by using a polymide substrate, as shown in Figure 6. The polymide material has 0.1-mm thickness and a dielectric constant (\(\epsilon\)r) of 3.5. As shown in Figure 7, the measuring setup contains VNA and waveguide ports. The FSS is placed between the waveguide ports of VNA to measure scattering parameters like S\({}_{11}\) and S\({}_{12}\) experimentally. To validate the proposed unit cell, ADS software is used in this article. Figure 8 depicts the s-parameters comparison among simulated and measured results. The equivalent circuit diagram for the proposed unit cell radiating patch was drawn in the ADS tool, which is shown clearly in Figure 9. For the circuit diagram, both the input side and output sides 50 ohms are connected to provide impedance matching and to obtain the desired output. Each SRR is represented by the combination of inductance and capacitance. The proposed unit cell contains two SRRs, namely square-type SRR and circular-type SRR. In the square-type split ring resonator, the inner SRR is represented by the L1 and C1 combination, as shown in Figure 9. The middle square-type SRR, which is between inner and outer square SRRs, is represented by L2 and C2, and the outer square-type SRR is represented by L3 and C3. The strips which are present between the square-type SRRs are represented by equivalent inductances like L6 and L7, the strip present between square-type SRR and circular SRR is represented by L8 and the gaps between lower to middle square-type SRR and middle to higher square-type SRR are represented by C4 and C5, correspondingly. Finally, the gap between square-type SRR and circular SRR is represented by C7. Once the circuit is designed in ADS software, the start and stop frequencies are selected as 7 GHz and 12 GHz, correspondingly, in the S-parameters section of the ADS tool with a step size of 100 MHz. By using the tuning option, in the ADS tool, the lumped parameters were tuned and obtained the return loss plot, as shown in Figure 10. For the CST-based unit cell simulation, the reflection coefficient resonates at 9.84 GHz and 11.46 GHz frequencies, with reflection coefficient values of \(-\)31.68 dB and \(-\)23.13 dB, correspondingly, and the transmission coefficient resonates at 8 GHz, 8.435 GHz and 11.11 GHz frequencies, with transmission coefficient values of \(-\)33.20 dB, \(-\)37.13 and \(-\)25.89 dB, correspondingly. By using ADS simulation, the reflection coefficient resonates at 9.9 GHz and 11.4 GHz, with reflection coefficient amplitudes of \(-\)47.82 dB \(-\)37.57 dB, correspondingly, and the transmission coefficient resonates at 7.83 GHz, 8.51 GHz and 11.40 GHz frequencies, with transmission coefficient amplitudes of \(-\)39.72 dB, \(-\)39.56 dB and \(-\)34.81 dB, correspondingly.
## 4 Dataset, Experimental Settings, and Evaluation
### Evaluation  
To verify the prediction performance of the proposed method, the RMSE, MAE, and MAPE were adopted as the evaluation indicators. The equations are as follows:  
\[RMSE=\sqrt{\frac{1}{C}{\sum\limits_{i=1}^{C}{(y_{i}-y_{i}^{\prime})}^{2}}} \tag{6}\]  
\[MAE=\frac{1}{C}{\sum\limits_{i=1}^{C}{\left\|{y_{i}-y_{i}^{\prime}}\right\|}} \tag{7}\]  
\[MAPE=\frac{1}{C}{\sum\limits_{i=1}^{C}{\left\|{\frac{y_{i}-y_{i}^{\prime}}{y_{ i}}}\right\|}} \tag{8}\]  
Here, \(y_{i}^{\prime}\) and \(y_{i}\) denote the predicted value and observed true value, respectively, and \(C\) is the number of all samples.
# Effect of Harmonics on Ferroresonance in Low Voltage Power Factor Correction System--A Case Study
## 1 Introduction
They consist of several capacitor stages, which are automatically switched in by the PFC controller to keep the displacement factor (\(cos\varphi\)) at the set point. Detuned reactors are connected in series to the power factor correction capacitors to prevent resonance, protect the capacitors and partially filter the current harmonics. The inductance value of the detuned reactor is selected so that the resonant frequency of an LC circuit is far away from the harmonic frequencies present in the power system. The measurement study presented in this paper was triggered by strange occurrences in the industrial plant after replacing two conventional drives with variable speed drives. The industrial plant operators noticed a loud audible noise in the detuned reactors of the PFC system and overheating of the detuned reactors, which visibly damaged the insulation of the reactors. This occurred under various operating conditions and could not be attributed to any particular configuration or operating condition. The main objective of the measurement study was to uncover the cause of the audible noise and overheating of the detuned reactors, show how replacing the conventional drives with variable speed drives affected this occurrence, and propose a solution to prevent it. Numerous real field tests were carried out in the transformer station with different operating conditions, and the results of the analysis are discussed in this paper. The experimental results show that the audible noise and overheating of the detuned reactors are caused by ferroresonance. In addition, the level of voltage harmonic distortion on the low voltage busbar affects the occurrence of ferroresonance. The change from conventional drives to variable speed drives resulted in sufficiently high harmonic distortion on the low voltage busbar at some operating points to cause the occurrence of ferroresonance. When the harmonic content on the low voltage side is high, the switching on of the capacitor stage causes the ferroresonance at the detuned reactor supported by the medium voltage system. During the ferroresonance, the harmonic distortions of the voltages and currents on the low voltage and medium voltage busbars are high, and the detuned reactor and PFC capacitor exhibit overvoltages and overcurrents. In this paper, the voltage and current waveforms on the low-voltage and medium-voltage side before and during the ferroresonance are analyzed, and the possible solutions to avoid the ferroresonance are discussed. The paper is organized as follows. Section 2 gives a theoretical overview of feroresonance. Section 3 describes the system studied and the conditions under which feroresonance occurs. Section 4 presents presents the case study results, gives the analysis of the currents and voltages in the industrial plant under feroresonance, and analyzes the influence of harmonic distortion on feroresonance. The conclusion is given in the Section 5.
# Quality Assessment of DSMs Produced from UAV Flights Georeferenced with On-Board RTK Positioning
## Materials and Methods
### Experiment Evaluation Methods  
The experiment's goals were to evaluate the repeatability and accuracy of DSM generated by UAV flights under different block control configurations. To this aim, the block orientation quality was first assessed on 14 signalised 3D checkpoints; then, the discrepancies among DSMs obtained under the same block control configuration were assessed, and then compared to those of different configurations. Finally, the DSM accuracy was evaluated comparing the elevations of a large set of checkpoints distributed over the area (see Figure 2) with the DSM elevation predicted at the same locations.  
Block orientation accuracy was evaluated by computing the differences between the coordinates of the signalised checkpoints estimated by a BBA executed with a given control configuration, and those measured by GNSS. Mean values, standard deviations, and the root mean square error (RMSE) of the differences were computed. The degree of repeatability was inferred from the RMSE ranges.  
DSM repeatability was measured by the discrepancy range among the DSM elevations produced from the four flights flown with the same RTK mode and adjusted with the same control configuration. DSM accuracy was evaluated by point-wise comparison of the DSM elevation with the elevation of a GNSS NRTK survey.  
The software package PhotoScan (v 1.2.6 build 2834) by Agisoft LLC, St. Petersburg, Russia was used for block orientation and DSM generation (the reader is referred to PhotoScan manual v.1.4 [47] for details on the processing parameters mentioned in Figure 1 and in the following). The processing parameters were kept unchanged, as this paper's focus is on the evaluation of block orientation accuracy and DSM uncertainty due to different GNSS datasets and imagery. A parameter optimisation on a case-by-case basis was therefore ruled out.
## 2 Materials and Methods
### Regression Models and Genetic Algorithm
#### 2.1.1 Neural Networks: Artificial Neural Network and Extreme Learning Machine  
Building upon the foundational work of [PERSON] and [PERSON] [70], [PERSON] [71], and [PERSON] et al. [72], artificial neural networks (ANNs) represent independent variables as nodes (neurons) in an input layer and dependent variable(s) as nodes in an output layer, with hidden layers in between to model nonlinear relationships. Each node in a layer connects to all nodes in adjacent layers, with assigned weights. ANNs use feedforward architectures trained via backpropagation, adjusting weights iteratively through gradient-based optimization methods. Activation functions transform the weighted inputs in the hidden layers into predictions, and biases enhance generalization. Despite their robustness and parallelizability, ANNs require careful architecture tuning and often display low learning efficiency.  
[PERSON] et al. [73] introduced the extreme learning machine (ELM) as an alternative, using a single-hidden layer where weights and biases are randomly assigned and fixed, avoiding iterative gradient descent. Only the weights between the hidden and output layers are adjusted. This method significantly accelerates training but is susceptible to instability, poor generalization, and sensitivity to outliers and imbalanced data.
## 2 Clinical Dataset, Experimental Setup, and Stochastic Sensitivity Analysis Procedure  
### Clinical Dataset  
The healthy aorta dataset was obtained from clinical measurements in [17]. Morphological and functional patient-specific data were acquired from MRI analysis of a healthy 28-year-old male. The aorta geometry, shown in Figure 1a, was obtained through segmentation of the MRI data. It is characterized by the ascending aorta, AA, the descending aorta, DA, and the three supra-aortic branches (brachioephalic artery, BCA, left common carotid artery, LCCA, and left subclavian artery, LSA). Figure 1b shows the waveform of the flow rate entering AA, which is acquired by processing the functional data obtained by means of phase-contrast MRI. The following acquisition parameters were set for PC-MRI: a flip angle of \(10^{\circ}\), a repetition time of 5.32 ms, an echo time of 3 ms, a \(2\times 2\times 2\) mm\({}^{3}\) isotropic voxel, and a velocity encoding (VENC) interval of 250 cm/s.  
The geometry of the healthy aorta was locally modified using a morphing procedure to generate two diseased geometries, viz. an aneurismatic aorta (Figure 2a) and a coarctated aorta (Figure 2b). The aneurysm develops in AA, and it is characterized by an increase in vessel diameter of about 60%. On the other hand, the coarctation is located in the descending branch with a decrease in the aortic diameter of up to 70%. The flow rate waveform from Figure 1b is also used as a reference for the diseased aortas.  
Figure 1: MRI-acquired (**a**) geometry of the healthy aorta and (**b**) inlet flow-rate waveform.
## 4 Experiments
### Effect of Model Updates  
LLMs, as exemplified by ChatGPT, undergo updates. Therefore, it can be assumed that patches may be applied against jailbreak attacks, leading to the loss of effectiveness of existing attacks with each update. A clear example is manual jailbreak attacks. The creation of jailbreak prompts manually is limited. Even if jailbreak prompts are effective at a certain point, they may easily be blocked in future updates, for instance, by being added to a blacklist. On the other hand, the proposed method, which considers creating jailbreak prompts anew from the target LLM, is expected to maintain its attack performance even after model updates.  
To evaluate the effect of model updates on our proposed method, we used three different snapshots of ChatGPT (GPT-3.5): March (gpt-3.5-turbo-0301), June (gpt-3.5-turbo-0613), and November (gpt-3.5-turbo-1106) 2023 versions. Although we applied our method to each of these snapshots around the same time, the effect of model updates could be appropriately evaluated because these snapshots were properly fixed and did not receive any updates during our experiments.  
We calculated the overall ASR for both the proposed method and manual jailbreak attacks for each snapshot of ChatGPT. The baseline ASR was also obtained. The results are shown in Figure 2. As expected, the ASR of manual jailbreak attacks decreased with model updates. Specifically, the ASR decreased from 77.2% in March to 66.1% in June, and then to 51.3% in November. This suggests that jailbreak attacks were mitigated by some measures taken by LLM vendors. However, the proposed method maintained an ASR of over 80% regardless of model updates. These results suggest that the proposed method is not affected by model updates, although continuous verification of the impact of future updates is necessary.  
Figure 2: Effect of model updates on attack success rate (ASR; %) of the proposed method (ours) and manual jailbreak attacks (MJA). Baseline ASR (BL) is also presented.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**AdvRephr/Target** & **GPT-3.5** & **Gemini-Pro** \\ \hline GPT-3.5 & 81.0 & 67.1 \\ Gemini-Pro & 65.1 & 83.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Attack success rate (ASR; %) for different combinations of models used for adversarial rephrasing (AdvRephr) and target models.
# ATC Human Factors Involved in RPAS Contingency Management in Non-Segregated Airspace
## 4 Results
### RPAS-TC Communication  
Communication between ATCO and RPAS is supported via two separate mechanisms: directly via the voice radiotelephony channel and indirectly via the ADS-C datalink exchanging the RPAS flight intent. When employing the radiotelephony channel, the PiC should switch frequencies, as each control centre has a dedicated frequency. On the contrary, the ADS-C datalink operates automatically, sending the flight-intent data stream to the appropriate ATCO control console.  
To communicate an emergency state of the RPAS and to update the development of the contingency, the PiC would employ the assigned frequency and should change the employed frequency as it crosses from one sector to another. In case of a lost link, it has been assumed that no further communication between PiC and ATCO can be maintained and only the flight intent may be available to the ATCO.  
Figure 9 shows the results of the operative questionnaires. The quality of the PiC-ATCO communications was evaluated throughout the operative questionnaire in Q2 and Q14; the functionality of the flight-intent information was evaluated in Q8.  
The results of Q2 indicate that the RPAS pilot can maintain a high-quality communication dialogue with the ATCO in the engine failure contingency scenarios. However, during the lost-link scenario, the efficiency in the communication was reduced, especially in the RQ4 mission, where there was no flight-intent support. In addition, for that particular question, its inconsistency was later uncovered, as some ATCO assumed that it was referring to the quality of communications after the lost link, while others only up until the moment the contingency occurred. Thus, a wide dispersion of results was observed. Q14 provides contradictory results related to the status of the transponder setup. The non-intent scenarios indicate that the transponder identifying the contingency state worked adequately, while intent scenarios provided mediocre ones. Upon consultation, ATCO indicated that they were expecting specific details of the consistency being present (not included at the time), thus producing low ratings.  
Q8 indicates that the ATCO perceived the use of datalink to update the RPAS flight intent as a positive tool that provided relevant information. The flight intent was valued in a highly positive way in the MQ-9 evaluation. However, the ratings were reduced in the RQ-4A mission evaluation. After discussion with the participating ATCO, it was identified that the RQ-4A mission involved a return to the origin airport from a high altitude. That return implied a long descent, crossing a wide range of altitudes. The designed flight intent did not provide ATCO with intermediate altitude reference points, which ATCO identified  
Figure 9: Subjective rating: ATC operational perception on RPAS contingency scenarios (see questions in Table 8).  
as extremely important to manage the conflicting traffic. Thus, they evaluated the flight intent as a powerful tool that required some level of refinement in its definition.
## 3 Satellite-to-Ground QKD Simulator
### Atmospheric Link Modeling
#### 3.2.5 Night-Time Sky Radiance and Urban Background Noise  
In FSO-QKD, much like in traditional communication methods, the effectiveness of a link is largely determined by the SNR. While conventional communication techniques can enhance SNR by boosting the signal strength, QKD operates differently since it relies on utilizing a single-photonic qubit to transmit information. Consequently, reducing noise becomes the sole strategy for enhancing SNR in QKD systems. The SNR, in this context, is quantified by comparing the average number of photons that carry the signal to the noise photons detected per pulse by the SNSPDs/SPADs. The primary sources of noise in these systems are dark counts and ambient light contribution. Dark counts, which are false counts by the detector in the absence of actual signal photons, can be minimized in SPADs to rates fewer than 25 Hz by operating them at the Geiger mode. Meanwhile, for SNSPDs, the dark count rate (DCR) can be reduced to values that are as low as the mHz scale [59]. However, in free-space paths, such as those used in satellite-to-ground QKD, the system is vulnerable to background radiation, which significantly contributes to noise [60]. Background radiation comes from natural sources like the sun, moon, and stars, whose light can be scattered by atmospheric elements such as molecules, aerosols, fog, and clouds, thereby increasing background noise. Although human-made light, like city illumination, also adds to the background noise, its impact can be minimized by strategically locating the OGS away from urban areas. In this work, we primarily addressed natural background light as a noise source during night-time (new moon) conditions, whereas in ref. [37], the background noise that was generated within urban areas, as well as daylight QKD, was discussed. The consideration of daytime conditions poses challenges due to the difficulty in mitigating the noise resulting from excessive solar radiance, and it is considered out of the scope for the present work. The background radiance that reaches the detectors can be provided by the following equation:  
\[P_{buck}=H_{rad}\times\Omega_{FOV}\times A_{r}\times\Delta\lambda, \tag{10}\]  
where \(H_{rad}\Big{(}\text{W}/\text{m}^{2}\text{sr}\text{\leavevmode\
obreak\
## 4 Protocol for Source Location Protection Based on Limited Flooding
### h-Hops Limited Flooding Initialized from the Source  
The limited flooding is started by the source \(s\) within the area \(h\)-hops away from \(s\). This has two purposes: (1) initialize the nodes within the flooding area about their distance to the source; and (2) exchange the distance information of each node with their neighbors.  
\begin{table}
\begin{tabular}{c c} \hline (K\({}_{\text{pub}}\), K\({}_{\text{ori}}\)) & The public/private key pair for packet encryption and decryption \\ E\({}_{\text{Kpub}}\)(m) & Encrypt packet m by public key K\({}_{\text{pub}}\) \\ bs & Base station \\ Hop\({}_{u,v}\) & The shortest distance from sensor u to sensor v measured by hops \\ p\({}_{\text{i}}\) & Phantom location \\ u.neighbor & The set of sensor u’ neighbors \\ u.set\_parent & \{v\(\mid\)v\(\in\)u.neighbor\(\cap\)Hop\({}_{v,b}\)\(<\) Hop\({}_{u,b}\)\(\}\) \\ r & The visual radius for the attacker \\ h & The random directed hops \\ \(Hop_{u}\) & The hops from sensor u to sensor v along the inferior arc \(uv\) \\ H & The shortest distance from source to the base station measured by hops \\ R & Transmission range of a sensor \\ \(SP_{u,v}\) & A path from sensor u to v \\ \hline \end{tabular}
\end{table}
Table 1: Notations used in this paper.  
Once a sensor detects the panda, it becomes the source. The source starts a flooding by sending a flooding packet {_BRO_SOURCE_, _ID_, _hop_s_} to all its neighbors, where _BRO_SOURCE_ and _ID_ denote the packet type and the unique identifier of the packet sender, respectively. The _hop_s_ is initialized to 0 and increased by 1 after each transmission. The flooding packet will be discarded once _hop_s_ achieves \(h\). Different from the broadcast performed in the network initialization process, here, _hop_s_ is used to record the shortest distance from the current sensor to the source instead of the base station. Since the flooding packet will only be transmitted when _hop_s_\(\leq\)_h_, the flooding area is limited within _h_-hops from the source. After the flooding, each sensor in the flooding area has its _D_s_ and knows the _D_s_ of each of its neighbors. After that, each sensor within the flooding area, say \(u\), can construct a set \(D\), which includes all its neighbors each of which has a larger _D_s_ than that of \(u\).
# Factors Affecting Implementation of Radiological Protection Aspects of Imaging in Radiotherapy
## 3 Results  
### Survey of IGRT Practices in Different Countries  
The countries included in the survey, together with the numbers of radiotherapy centres and linacs, and the HDI are given in Table 1. The proportions of radiotherapy centres from which data were obtained in countries with HDI values of 0.9 and above were 11% or less, but for five countries with HDI values of 0.74-0.90, for which less data are available in the literature, results were obtained for 24% or more of the centres. However, the survey can only be considered as a snapshot showing variations in practices that occur in different regions, rather than being representative of the particular countries, as the centres participating might not be representative of the situation in the whole country.  
All the centres used CT as the main imaging modality for treatment planning. Other imaging modalities were used in the planning process, with 80-90% of centres in Germany, Australasia, the USA, and Cyprus using magnetic resonance imaging (MRI). Positron emission tomography (PET)/CT was used when appropriate in 65-80% of centres in Germany, Australasia, the USA and Cyprus, and 40-50% of those in the Asian and South American countries. There were significant declines with the country HDI values in the proportions of centres using MRI (R = 0.879, \(p\) = 0.002) and PET/CT (R = 0.929, \(p\) < 0.0005 (Table 2)) [14]. The use of other modalities (SPECT/CT and ultrasound) showed weaker correlations (R < 0.75) and/or no statistical significance (\(p\) > 0.05). Many of the imaging modalities used were sited elsewhere in the hospital complexes, but between 20% and 50% of hospitals in the European, African, and American countries had MRI scanners within the radiotherapy centres.