esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Evaluating a Black Start Scheme's Performance  
Quantifiable approaches which aim to build optimal partition and start-up sequence schemes have been proposed in [7; 35; 43]. In them, the performance of the proposed scheme is evaluated by taking into account the ramp rate of the black start units, the generation capability curve of the system, and its restoration efficiency, according to Equation (1).  
\[f_{1}=\frac{\sum\limits_{i=1}^{N_{C}}b_{i}\int\limits_{k\Delta t}^{T}P_{Gi}(t) dt+\sum\limits_{i=1}^{N_{DC}}c_{i}\int\limits_{k\Delta t}^{T}P_{DCi}(t)dt}{T} \tag{1}\]  
where:  
* \(f_{1}\) is the efficiency of the black start scheme
* \(b_{i}\) is the restarting state indication of the unit \(G_{i}\)
* \(T\) is the total time consumption of units
* \(\Delta t\) is the time-stepping interval
* \(N_{G}\) are the units to be restored
* \(P_{Gi}(t)\) is the MW output of the unit \(G_{i}\)
* \(N_{DC}\) is the number of HVDC converter stations to be restored
* \(c_{i}\) is the restarting state indication of the HVDC converter station \(DC_{i}\)
* \(P_{DCi}(t)\) is the transmission power of the HVDC converter station \(DC_{i}\)  
If \(G_{i}\) or \(DC_{i}\) are restarted in the \(k\)th time step, \(b_{i}\) and \(c_{i}\), respectively, are 1; otherwise, they are 0.  
From Equation (1) it can be assured that obtaining a faster increase on load capability and minimizing the time required for this process is the key for a successful restoration.  
As shown in Figure 2, the behaviour of a black start scheme's increase in its possible power output can be modelled as a curve, which is very useful for analyzing its performance and comparing it to other proposals [43]. Typical generation capability curves have a resemblance to individual units' ramp rates, showing the initial cranking time of most of the units, the slope at their ramping stage, and the stabilization at the end of the restoration.  
Figure 1: Ramp rate periods for a standard generating unit. The ramp rate is the increase of load that a generating unit can provide with respect to time.  
On a practical decision-making procedure of whether assisting or not during black start, there will always be uncertain factors. Since the 1980s, development and optimization of black start schemes have been explored in [7; 35; 43] and this continues as future power networks will demand a quicker and more accurate response to power outages. Thus, one of the most promising tools that can provide this performance enhancement is VSC-HVDC technology.
## 2 SPME Techniques
### Cooled Coated Fiber Device  
Apart from in-tube SPME, procedures using a cooled fiber and other methods that do not necessarily rely on the use of a fiber have emerged. These new developments will be explained in the following section.  
A recent development in the application of HS-SPME is an internally cooled coated fiber device (CCF) or cold fiber HS-SPME device. The main reason for developing this method was the improved release of analytes from the interfering phases in complex matrices [52, 53].  
For desorption of analytes from solid particles it is often useful to increase the extraction temperature. However absorption of analytes by the fiber coating is an exothermic process, therefore this process leads to a decrease of the partition coefficients [2]. The aim is to accelerate the mass transfer process and simultaneously increase the distribution constants of analytes [2]. For that purpose, an internally cooled coated fiber device was developed heating the sample matrix at the same time as the fiber coating is cooled down. Especially for matrices with high viscosity or for volatiles with low partition coefficients CCF is useful [2]. This technology was successfully employed for the first time to extract analytes from various environmental matrices [52, 53, 54]. CCF was also successfully applied to food analysis, such as extracting volatiles and semivolatiles from tropical fruit pulps by [PERSON] and [PERSON] (2006). They stated that the cold fiber was the most suitable fiber for the purpose of extracting volatile compounds from the five fruit pulps studied [55]. Additionally, it was reported that cold-fiber HS-SPME offers more sensitivity and higher sample throughput than conventional HS-SPME [56]. One drawback, however, of the increased fiber capacity through this cooled coated fiber is the loss of selectivity [2]. Not only the analytes but also the interferences are extracted exhaustively onto the coating [6]. CCF has been miniaturized and automated first by [PERSON] and [PERSON] (2006) [53] and is nowadays routinely used (see Figure 2) [2].
# Cartesian Constrained Stochastic Trajectory Optimization for Motion Planning
## 5 Discussion  
In this paper, we proposed two extensions for the widely used STOMP algorithm. These extensions enable STOMP to be used as a cartesian space planner. This will allow it to be used in a variety of applications such as liquid handling or ones that use a vacuum gripper.  
Both extensions, Cartesian Normal Distribution Sampling Generator and the Cartesian Distance Cost Function, were tested in an environment that was comparable to common industrial applications such as pick and place or bin picking. The results were compared  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Criterion**}} & \multicolumn{4}{c}{**Cartesian Noise**} & \multicolumn{4}{c}{**Joint Noise**} \\  & \multicolumn{2}{c}{**Without Tolerance**} & \multicolumn{2}{c}{**With Tolerance**} & \multicolumn{2}{c}{**Without Tolerance**} & \multicolumn{2}{c}{**With Tolerance**} \\  & **Avg** & **Var** & **Avg** & **Var** & **Avg** & **Var** & **Avg** & **Var** \\ \hline Cartesian Distance (m) & 2.4668 & 0.5871 & 2.035 & 0.244 & 1.6482 & 0.1728 & 1.4897 & 0.0129 \\ \hline Tool Orientation (rad) & 0.4750 & 0.0052 & 0.383 & 0.001 & 1.5697 & 0.3733 & 0.3884 & 0.0012 \\ \hline Computation Time (s) & 0.2267 & 0.0054 & 0.656 & 0.058 & 0.135 & 0.0031 & 0.4369 & 0.0710 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of results’ averages and variances.  
with the standard STOMP algorithm. Both experiments showed that these modifications led to desirable results. However, it should be noted that in complex environments where the STOMP algorithm does not find a suitable trajectory with 100% success rate, our extensions lower the success rate even further [25]. This is caused by a further limitation of the possible state space. Compared to STOMP modifications such as GSTOMP, our extensions are based on STOMP framework, so there is no need to use other third-party libraries that allow easier use. Another limitation of our proposed extensions is the increase in computational time needed to find a suitable trajectory.  
We hope to address these issues in our future work by optimizing the sampling generator, which will lead to fewer needed sample-state generations, thus lowering computational time. Additionally, we want to compare our extension of STOMP with path-planning algorithms such as RRT, PRM, CHOMP, etc.
## 1 Introduction
All of these techniques however, result in modest improvements because the interaction between nonlinearity and random noises in the network, such as the noise originating from optical amplifiers, adds significant stochastic nonlinear distortion. Moreover, MS-OPC reduces the flexibility in an optical routed network, IVSTF presents a marginal performance benefit, and DBP is very complex forbidding potential implementation in real-time. On the other hand, enhance signal capacity in PCSC, modified versions have been proposed in [14; 15], which however offer marginal performance benefits or still sacrifice spectral-efficiency. More drawbacks of these techniques are summarized in Section 2. Machine learning is the combination of pattern recognition [16; 17] and the theory that computers can learn without being programmed to perform specific tasks. The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the provided examples. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly. Machine learning has been recently under the spotlight for many photonic-related applications [18; 19]. In long-haul CO-OFDM several supervised and unsupervised machine learning algorithms (MLAs) have been harnessed to mainly perform DSP-based fiber-induced nonlinearity compensation, including artificial neural network (ANNs) [20; 21; 22; 23; 24; 25; 26], support vector machine (SVMs) [27; 28; 29; 30; 31; 32; 33; 34], and machine learning clustering such as Fuzzy-logic C-means (FL or FLC) [35], K-means [35] or affinity propagation (AP) [36]. In this paper we review the aforementioned MLAs for CO-OFDM, showing key results for single-polarization and standard SMF (SSMF)-based long-haul transmission by comparing them with full-step DBP (FS-DBP) and IVSTF. We also briefly discuss the operation of popular deterministic nonlinearity cancellation techniques and a full computational complexity analysis is presented, for the first time, among key MLAs, FS-DBP and IVSTF.
## 1 Introduction
### Study Area  
South Asia covers 3.4% of the world's geographical area and holds 24% of the world's population. A most important region of interest is the Indo-Gangetic Plain (IGP), which is adversely affected both at the local and climatic levels in a number of ways. It is also known as Indus-Ganga and the North Indian River Plain, stretching from the Arabian Sea to the Bay of Bengal and from the Himalayan foothills to the Indian peninsula. For Pakistan (33 deg40 degN and 73 deg10 degE), 24% of its land and 86% of its population belongs to the IGP region [42].  
Pakistan shares its borders with India on the East, Iran on the West, China in the North and Afghanistan on the north-western side, as depicted in Figure 1[43; 44]. Pakistan is situated in a meteorological zone called the subtropical high region and is largely affected by the movement of both inter-continental and regional air masses coming from the Middle East, Afghanistan, India and the African continent. The surface elevation ranges from the Arabian Sea (in the south) to the world's second highest (8611 m) peak of K-2 in the north [45].  
Seasons in Pakistan are defined on the basis of the occurrence of the monsoon: pre-monsoon (March-May), monsoon (June-September), post-monsoon (October-November) and winter (December-February) [46]. The intensity of winter follows with January being the coldest month, followed by December and February [47].  
One of the major environmental impacts observed over this region is the frequent occurrence of persistent winter fog, particularly during the months of December and January. Towards developing a comprehensive understanding and to study the impacts of persistent winter fog across the IGP, a multi-year and multi-country project was initiated by the International Centre for Integrated Mountain Development (ICIMOD) based in Kathmandu, Nepal. As a part of this initiative, this study had a special focus on Lahore city. It also included ground observations conducted for the 2014-2015 winter period within Lahore city (Figure 1) along with satellite data analysis for the 2012-2015 winter periods.
## 6 Discussion  
The results in figures and also in tabular form in Table 3 demonstrate that the power consumption is reduced when using P-BIOSARP because, as described in the methodology (Section 3), the maximum weight is given to the energy parameter when calculating and making decisions with the most optimal node. The best node is selected from the neighboring nodes, with the node with the highest remaining battery chosen over the rest.  
In Figure 6, the \(\mathit{batt}_{ec}\) of P-BIOSARP is far better than E-BIOSARP when the simulation was run with 121 nodes for 300 s. This shows that if the network is increased with more nodes and needs to be deployed for a longer period, \(\mathit{batt}_{ec}\) will become increasingly better. In addition, the PDR is a bit lower in Figure 7, and the traffic overhead is higher in Figure 8, which is a shortcoming of giving a large rise to a single property, and it eventually depends on the application requirements.  
Furthermore, in the scenario where the malicious nodes are increased from 4 to 20, on average, \(\mathit{batt}_{ec}\), as shown in Figure 9, is less required with P-BIOSARP than with E-BIOSARP. However, the PDR in Figure 10 and traffic overhead in Figure 11 are a little bit higher. This shows that when a high level of security is required, parameters that can raise the quality of traffic all need to be enabled.  
In the scenario with a maximum of 121 nodes in an 80 m\({}^{2}\) area and run for 300 s where the packet rate increases from 0.33/s to 3.6/s, Figure 12 shows that there is a significant difference in \(\mathit{batt}_{ec}\), which is an average of 17% less with P-BIOSARP compared to the E-BIOSARP, BIOSARP, and SRTLD routing protocols. The PDR is lower, as shown in Figure 13, and PO is high, as shown in Figure 14, with P-BIOSARP. Analyzing the trends in Figures 12-14 shows that with more traffic in the network, P-BIOSARP performs much better in all aspects, whether \(\mathit{batt}_{ec}\), PDR, or PO is considered.  
In the case where the scenario is configured with 20 malicious nodes and 12 mobile nodes with source nodes, meaning with a full load on the network routing protocols to establish and route the data packets, the P-BIOSARP results are far better than those of the other routing protocols in terms of \(\mathit{batt}_{ac}\), as shown in Figure 15, PDR, as shown in Figure 16, and PO, as shown in Figure 17. Hence, the results prove that when the network is heavily loaded and needs to save energy, P-BIOSARP should be employed. Otherwise, when high quality is required for data routing, parameters related to reliability should be considered when making the optimal decision. P-BIOSARP is able to secure the FSN from spoofing, HELLO flood attacks, Sybil attacks, eavesdropping, selective forwarding, acknowledgment spoofing, brute-force or exhaustive key search, replaying or altering of routing information, data-tampering attacks, and cloning, as it is the enhanced version of [38].
# Fast-RRT: A RRT-Based Optimal Path Finding Method
## 4 Simulation and Result  
In order to evaluate the performance of the proposed algorithm, a series of experiments are performed. As mentioned above, the Fast-RRT includes two modules, Improved RRT and Fast-Optimal. The Improved RRT algorithm can quickly find the initial path, while the Fast-Optimal algorithm can obtain a near-optimal path by combining the initial paths. Therefore, we first compared Improved-RRT with RRT to prove its efficiency in finding the initial solution. Then the comparison between Fast-Optimal and RRT is shown to prove the efficiency of the algorithm in finding the near-optimal solution.  
Specifically, three experiments are designed to evaluate the efficiency of the proposed algorithm. The first experiment is to find a feasible path in a complex environment. The simulation environment is shown in Figure 9, which contains multiple obstacles of different shapes. The second experiment is to find a feasible path in a narrow passage scene, which is difficult for RRT. The simulation environment containing multiple narrow passages is shown in Figure 10. The third experiment is to find a near-optimal path by using the same  
Figure 8: Schematic illustration of the process of path fine-tuning. _point1_, _point2_, _point3_ are the intersection points of path fusion. Since the connection between _point1_ and _point2_ collides with obstacles, the original path is retained. In contrast, the connection between _point2_ and _point3_ is unobstructed so that the original path can be replaced by a new straight path.  
environment as the first experiment. All experiments were run on a desktop computer, configured with Intel Core i7-7700k processor, 16 GB RAM, Windows 10, and Matlab R202.
## 2 Materials and Methods
### RF Inactivation Trials
#### 2.3.2 RF Inactivation Treatments  
Sterile glass vials containing 2700 \(\upmu\)L of broth (i.e., BHI and TSB for _L. monocytogenes_ and _S. Typhimurium_, respectively) were inoculated with 300 \(\upmu\)L of the second preculture of the microorganisms, resulting in an initial cell population of approximately 8 log\({}_{10}\) CFU/mL. Based on preliminary experiments, maximum treatment times of 20 min were selected for both microorganisms. After RF treatment, samples were stored in ice water to stop microbial inactivation prior to further processing. Serial decimal dilutions were directly prepared from an aliquot of the samples using 0.85% (\(w/v\)) saline solution (NaCl, Sigma Aldrich, Diegem, Belgium), and plated on Brain-Heart Infusion agar (BHIA) and Trypic Soy Agar (TSA) using the drop technique [22] for _L. monocytogenes_ and _S. Typhimurium_, respectively. Prior to enumeration, plates were incubated for at least 24 h at 30 or 37 \({}^{\circ}\)C, for _L. monocytogenes_ and _S. Typhimurium_, respectively.
## 3 Frequency Characteristics of Radiation Noise under Various Rotational Speeds
### Changes in Radiation Noise at Certain Frequencies  
In this section, the changes in radiation noise at certain frequencies were studied. TSPLs of 16 monitoring points were calculated and the results of TSPLs at 500, 1000, 2000, 4000, 8000 Hz with various rotational speeds were compared and are presented in Figure 6.  
Figure 5: The ratio of acoustic energy in different frequency ranges.  
As shown in Figure 6, TSPLs at 1000 and 2000 Hz are higher than the others, while TSPL at 8000 Hz is lower than the others under all of the operational conditions. Additionally, there is a TSPL turning point at 500 Hz when rotational speed is set as 2500 rpm, which is the 2-order BPF under 2500 rpm. Moreover, TSPLs at various frequencies show an ascending trend with the increase in rotational speed and the increase in TSPL at high frequency is higher than at low frequency. More specifically, when rotational speed increases from 1700 to 2900 rpm, TSPLs at 500, 1000, 2000, 4000 and 8000 Hz increase 8.72%, 13.46%, 10.77%, 14.01% and 17.66%, respectively. On the one hand, with the increasing of rotational speed, the pressure fluctuation intensity on wall surfaces consequently increases [32], so TSPLs at various frequencies increase simultaneously. On the other hand, as the rotational speed increases, the required net positive suction head (NPSHr) also increases, however, the available net positive suction head (NPSHa) has different change rules, and is defined as,  
\[NPSHa=\frac{p_{s}}{\rho g}+\frac{v_{s}^{2}}{2g}-\frac{p_{v}}{\rho g} \tag{8}\]  
where \(p_{s}\), \(v_{s}\) and \(p_{v}\) represent the inlet pressure (Pa), inlet velocity (m/s) and saturated vapor pressure at the corresponding temperature (25 \({}^{\circ}\)C). The NPSHa and NPSHr change patterns under various rotational speeds are presented in Figure 7.  
As shown in Figure 7, with an increase in rotational speed, the NPSHa decreases immediately and is even lower than NPSHr under 2700 and 2900 rpm. The decrease in NPSHa can lead to the development of cavitation. Furthermore, the development of cavitation can cause both pump body vibration [33] and acoustic pressure pulsation [34], especially in high frequency ranges, and further cause a dramatic increase in radiation noise at high frequencies.  
Figure 6: Changes in total sound pressure level (TSPL) at certain frequencies under various rotational speeds.
# MPC for Optimized Energy Exchange between Two Renewable-Energy Prosumers
## 2 Problem Formulation
### Battery Storage  
Using \(k\) to represent hourly time steps (\(k=1,2,3,\dots\)), the two battery states of charge \(SOC_{R}(k)\) and \(SOC_{C}(k)\), both in kWh, are combined into a single vector,  
\[x_{m}\left(k\right)=\left[\begin{array}{c}SOC_{R}\left(k\right)\\ SOC_{C}\left(k\right)\end{array}\right].\]  
This vector will become part of the state-space model, and it is used to construct a recursive matrix algebra update for the batteries, with the following equation and definitions.  
\[x_{m}\left(k+1\right)=x_{m}\left(k\right)+b_{m}u\left(k\right), \tag{1}\]  
\[u(k)=\left[P_{2R},\ P_{2C},\ P_{3R},\ P_{3C},\ P_{4R},\ P_{4C},\ P_{RC},\ P_{CR},\ P_{BRC},\ P_{BCR}\right]^{\top},\]  
\[b_{m}=\begin{bmatrix}0&0&\eta_{C}&0&-\eta_{D}&0&0&0&0&0\\ 0&0&0&\eta_{C}&0&-\eta_{D}&0&0&0&0\end{bmatrix}.\]  
In these definitions, \(\eta_{C}\) and \(\eta_{D}\) are the charging and discharging efficiencies for energy transfers to the battery \(P_{3R}\),\(P_{3C}\) and from the battery \(P_{4R}\),\(P_{4C}\), respectively. The vector \(u(k)\) is defined as the control vector for the system, and it contains all the energy flows that the algorithm must choose at each time step. These flows are all functions of \(k\), although this is not shown to simplify the expressions.
# An Audio Watermarking Algorithm Based on Adversarial Perturbation +
###### Abstract  
Recently, deep learning has been gradually applied to digital watermarking, which avoids the trouble of hand-designing robust transforms in traditional algorithms. However, most of the existing deep watermarking algorithms use encoder-decoder architecture, which is redundant. This paper proposes a novel audio watermarking algorithm based on adversarial perturbation, AAW. It adds tiny, imperceptible perturbations to the host audio and extracts the watermark with a pre-trained decoder. Moreover, the AAW algorithm also uses an attack simulation layer and a whitening layer to improve performance. The AAW algorithm contains only a differentiable decoder, so it reduces the redundancy. The experimental results also demonstrate that the proposed algorithm is effective and performs better than existing audio watermarking algorithms.  
audio watermarking; deep learning; adversarial example +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Real-Time State Evaluation System of Antenna Structures in Radio Telescopes Based on a Digital Twin
## 5 Virtual and Real Mapping
### Mechanical State Mapping  
The technical route of mechanical state mapping is shown in Figure 11.  
Figure 11: Mechanical state mapping.  
Figure 10: Coordinate relationship diagram.  
The RF surrogate model trained in Section 4.3 is packaged into PKL format and stored. After real-time sensor data transmission, the predicted deformation values are output and constructed into a color array through TCP/IP communication between Pycharm and Unity3D. By normalizing each node's predicted value between the maximum and minimum deformation range, the values are linearly mapped to the HSV hue spectrum from red (H = 0\({}^{\circ}\)) to blue (H = 240\({}^{\circ}\)). The Unity shader implements this color conversion with vertex data transmission and HSV-to-RGB transformation in the patch function, combined with vertex color interpolation defined in Equation (20). This process drives the mesh model nodes to generate finite element cloud images reflecting real-time deformation patterns. The conversion equation is as follows [26]:  
\[c=\frac{2}{3}\bigg{(}1-\frac{r-min}{max-min}\bigg{)} \tag{20}\]  
where \(c\) represents the HSV color value, \(max\) and \(min\) are the upper and lower limits within the set of predicted values, respectively, and \(r\) is the predicted result value of the current node.