esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparison of Image Normalization Methods for Multi-Site Deep Learning
## 5 Conclusions  
The performance of six different normalization methods was evaluated for different deep learning tasks for LARC patients in a multicenter setting with data from six different centers. Different scenarios were tested with training on data from all centers, on data from all centers except one, and from data from a single center. In this way, the influence of the normalization method on generalizability can be assessed.  
Normalization is important if the data are inhomogeneous, especially if the data set is small and the network is applied to data from a site not included in the training set. It plays a larger role in classification and regression than in segmentation.  
Our results show that percentile normalization followed by histogram matching performed the best for tumor segmentation and prediction of treatment outcomes in locally advanced rectal cancer. Setting the mean and standard deviation to a fixed value, which is often done for images, performed significantly worse than most other methods.  
Using deep learning approaches did not lead to any improvements over classical methods in most cases, and was only slightly better when training on data from a single center.  
Normalization improved the generalization of the network for different tasks, but there are limits to what can be corrected with normalization. It is essential to standardize data acquisition for routine clinical imaging for the widespread application of deep learning in clinics. With the current data available, it is not possible to predict clinically relevant outcomes of neoadjuvant treatment for patients with LARC.  
Conceptualization, S.A. and F.G.Z.; Methodology, S.A.; Software, S.A.; Validation, S.A.; Formal analysis, S.A.; Investigation, S.A.; Data curation, S.A., B.D.W. and A.M.; Writing--original draft, S.A.; Writing--review & editing, S.A., B.D.W., W.Z., J.H. and F.G.Z.; Visualization, S.A.; Supervision, J.H., U.I.A., L.R.S. and F.G.Z.; Funding acquisition, J.H., U.I.A. and F.G.Z. All authors have read and agreed to the published version of the manuscript.  
We gratefully acknowledge the support by German Research Foundation (DFG) through the grant 428149221 as part of the Radiomics priority program (SPP 2177) and data storage SDS@hd supported by the Ministry of Science, Research, and the Arts Baden-Wurttemberg (MWK) and the DFG through the grants INST 35/1314-1 FUGG and INST 35/1503-1 FUGG.  
The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee II of Heidelberg University (2017-571N-MA).  
Informed Consent Statement: Informed consent was waived due to the retrospective nature of this work.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to patient privacy.  
The authors declare no conflict of interest.
# RST Resilient Watermarking Scheme Based on DWT-SVD and Scale-Invariant Feature Transform
## 4 Experimental Results and Analysis  
In this section, performances in watermark robustness and imperceptibility of the proposed method are given and analyzed. This scheme is tested on the image with size of 512 \(\times\) 512, and the watermark applied in this scheme is a binary image with size of 64 \(\times\) 64. The scheme is tested on MATLAB R2014a with an Intel (R) Core (TM), Santa Clara, TX, USA, i3-2100 3.10 GHz CPU, 6 GB memory computer.  
The proposed scheme is performed on a number of standard images, which are shown in Figure 8.  
The Lena and Barbara images are selected for representative in this scheme. Compared with the Lena image, the Barbara image has more texture information. Similarly, considering symmetry and asymmetry of watermarks, the English character and logo images of Shandong University, China, set as \(w_{1}\) and \(w_{2}\), are applied in this scheme for representation. In addition, the character watermark is embedded into the host image Lena, and the logo image watermark is embedded into the host image Barbara.  
Figure 8: Test images: (**a**) Airplane; (**b**) [PERSON]; (**c**) [PERSON]; (**d**) Bank; (**e**) Announcement; (**f**) Fishing Boat; (**g**) Milkdrop; (**h**) Baboon; (**i**) Boat; (**j**) Bridge; (**k**) [PERSON]; (**l**) Peppers; (**m**) Goldhill; (**n**) Model; (**o**) Mountain; (**p**) Zelda.
# Tailored Magnetic Linear Birefringence in Wedge-Shaped Co Nanocluster Assemblies
## 2 Materials and Methods
### Surface Magnetization Characterization  
The magnetization, M, of those 2D nanocluster assemblies was first characterized with an experimental setup to investigate the magneto-optical Kerr effect (MOKE) [22]. The He-Ne laser beam (633 nm, 1 mW) was focused on the center of the sample surface at an incidence angle of 45\({}^{\circ}\) with respect to its normal axis, and a spot diameter \(\sim\)0.1 mm. By means of a Glan-Thompson polarizer, the polarization plane of the beam was oriented parallel to the incidence plane, and perpendicular to the magnetic field, \(H\). This so-called transversal MOKE geometry allowed us to simultaneously determine both in-plane magnetization components: parallel (M\({}_{L}\)) and perpendicular (M\({}_{T}\)) to \(H\), respectively. Later, for the transmission magneto-optical effect, the same experimental setup was modified according to the Voigt configuration, i.e., the laser beam impinged perpendicularly on the sample and with a relative orientation of 45\({}^{\circ}\) between the polarization plane and \(H\) direction [10; 23]. In both configurations, the reflected/transmitted light was separated with a Wollaston prism into its respective parallel and perpendicular components with respect to \(H\) direction. Their corresponding intensities, T\({}_{\parallel}\) and T\({}_{\perp}\), were recorded with two fast detectors and a differential amplifier circuit. Once the external magnetic field is applied, the rotation of the polarization plane, \(\delta\), can be obtained through the generic expression sin2\(\delta\) = (T\({}_{\parallel}\) - T\({}_{\perp}\))/T\({}_{0}\), where T\({}_{0}\) is the intensity of the incident light. The angular dependence of both Kerr and Voigt effects was studied by rotating the sample holder around its normal axis. In this work, the oscillating field was always applied in the substrate plane with an amplitude \(H_{max}=\pm\)150 mT and a sweep rate of 35 Hz.
# Effect of MXene Nanosheet Sticking on Supercapacitor Device Performance
## 3 Factors Influencing Sticking in MXene Electrodes
### Surface Chemistry of MXene Flakes
#### 3.2.2 Hydrophobicity
Moreover, a post-irradiation decrease in the atomic percentage of oxygen was noted, indicating the rejection of oxygen groups from the sample surface. It is worth noting that achieving a balance in surface chemistry is crucial. Extreme hydrophobicity can lead to poor dispersion and reduced accessibility of active sites, adversely affecting the electrode's performance. Understanding the influence of surface chemistry and hydrophobicity on sticking in MXene electrodes aids in designing effective surface modification strategies that promote better dispersion and prevent agglomeration. By tailoring the surface properties, researchers can enhance the stability and performance of MXene-based supercapacitor electrodes. Figure 8: Modification process of Ti\({}_{3}\)C\({}_{2}\) flakes for changing the wetting angle against water [55]. [PERSON] et al., Mater. Adv., copyright (2021), RSC. Figure 9: Dependence of the contact angle and conductivity of 2D multi-layered MXene (Ti\({}_{3}\)C\({}_{2}\)T\({}_{x}\)) vs. ion fluence [56]. Reprinted from [PERSON]. [PERSON] et al., Surf. Interf., copyright (2023), with permission from Elsevier.
# Experimental Test of Continuous Wave Frequency Diverse Array Doppler Radar +
## 4 Data Collection III: Moving Targets
### Experimental Results
#### 4.3.3 CA-CFAR Detection  
CFAR is a statistical target detection technique that uses adaptive thresholding with a sliding window approach to detect targets while maintaining a consistent false alarm rate. Our scene background is an open area and is assumed to be homogeneous. This assumption is supported by the \"No Target\" curves in Figures 12 and 13, which display a uniform background outside of the stationary reflections at the carrier frequencies \(f_{n}\).  
From ([51] Chapter 16.5), the maximum likelihood estimate of the interference power \(\vartheta_{i}^{2}\), the CA-CFAR constant \(\alpha_{CA}\), and the CA-CFAR threshold \(T_{CA}\) are defined as  
Figure 13: Frequency information from a drone flying toward (red) and away (yellow) from the FDA radar system with operational parameters \(f_{0}=2\,\mathrm{GHz}\), \(\Delta f=10\,\mathrm{kHz}\), and \(N=3\). Note that the CFAR detections are emphasized with a red diamond for visibility.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test** & **Radial Motion** & \(|\mathbf{v_{\text{measured}}}|\)**m/s** & \(|\mathbf{v_{\text{true}}}|\)**m/s** \\ \hline Car & \(+v_{0}\) & 6.37 & 6.71 \\ Car & \(-v_{0}\) & 5.62 & 6.26 \\ Drone & \(+v_{0}\) & 2.81 & 3.13 \\ Drone & \(-v_{0}\) & 2.81 & 3.13 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Radial velocities calculated from the Doppler shifts shown in Figures 12 and 13.  
\[\sigma_{i}^{2}=\sum_{m=1}^{M}z_{m}, \tag{18}\]  
\[\alpha_{CA}=(P_{fa}^{-1/M}-1),\text{ and } \tag{19}\]  
\[T_{CA}=\alpha_{CA}\delta_{i}^{2}, \tag{20}\]  
respectively, where the test cells \(z_{m}\) are indexed from \(m=1,2,...,M\) with \(M\) total test cells, and \(P_{fa}\) is the desired probability of false alarm for the detector.  
Overlaid in Figures 12 and 13 are CA-CFAR detections. We use \(M=16\) total test cells, \(P_{fa}=10^{-6}\), and two guard cells on each side of the cell under test. We notch out \(\pm 10\) Hz on either side of the zero-Doppler frequencies to suppress stationary scene clutter. The zero-Doppler frequencies are found via the peaks of the \"TX Copy\" curves.
# Optimizing Charging Pad Deployment by Applying a Quad-Tree Scheme
## 3 Optimizing the Charging Pad Deployment Problem by Applying the Quad-Tree Scheme
### Problem, Notation and System Model  
#### 3.1.1 Network Model  
In a WRSN, many rechargeable sensors, \(S=\left\{s_{0},\ s_{1},\ \ldots,\ s_{n}\right\}\), are randomly scattered in an area. These sensors collect specified information in the network and transmit it to the _base station_ (BS). When the battery level of the sensor is low, the BS dispatches drones to the sensor location for charging. Considering the limited power of a drone, it may not be able to fly to the sensor location, perform charging tasks, and then fly back to the BS. Therefore, it is necessary to configure some charging pads in the network area, \(P=\left\{p_{0},\ p_{1},\ \ldots,\ p_{m}\right\}\), so that the drone can replenish power on the charging pads to complete the task. In this article, some additional assumptions about the network are as follows:  
1. There is only one BS and one drone in the WRSN.
2. Sensors are homogeneous, static, and have the same battery capacity.
3. The BS knows the location of each sensor.
4. When a sensor's battery level is low, the BS receives a notification and subsequently dispatches the drone for charging. Once the drone completes the charging process, it returns to the BS.
5. The BS is located in the center of the network, does not move, and has unlimited power. Likewise, the charging pad does not move and has unlimited power.  
In this paper, we use a 2D map to illustrate the positions of sensors, charging pads, and base stations. Figure 1 depicts an example network layout. The drones can fly directly from the base station to sensors 3, 7, 8, and 9 for recharging tasks before returning. Alternatively, they can fly to pad \(1/2\) for a full recharge before proceeding to sensors 3, 4, 6, and 7/sensors 1, 2, 5, and 9 for recharging tasks, and then return to pad \(1/2\) to be recharged before flying back to the base station. Upon reaching the sensor locations, the drone hovers to charge the sensors.
## 4 BER Analysis
### Effects of Different SIR on BER Performance  
First, we consider BER performances in different SIR cases. Outwardly, according to Section 3.2, the impulse amplitude of jumping part increases with the decrease of SIR, but simultaneously the amplitude of non-jumping part corresponding to \"0\" is also improved, _i.e.,_ the relative amplitude differences remain unchanged in different SIR cases. Theoretically, the responses of code \"0\" and \"1\"signal to filter are with Rice amplitude distributions, whose probability density function (pdf) is \(p_{0}(r)\) and \(p_{1}(r)\), respectively, just having different mean values, _i.e.,_ existing the amplitude difference \(\Delta A\), so the BER formula of EBPSK has been deduced based on the classic detection theory [13]. Based on the binary detection model, the sketch map of the detection performance illustrated by pdf is shown in Figure 5, where the shaded area indicates the BER. In co-channel interference and small initial phase cases, the basic amplitude \(A_{0}\)increases, _i.e.,_ both the two pdfs move to the right, but the amplitude difference \(\Delta A\) remains unchanged, _i.e.,_ the relative location remains unchanged so that the overlapped area between the two pdfs is also unchanged, therefore, the BER performance does not change in different SIR cases by present amplitude detection method. On the other hand, in the large initial phase cases, the amplitude difference \(\Delta A\) decreases, _i.e.,_ the distance of \(p_{0}(r)\) and \(p_{1}(r)\) is closer, so the overlapped area becomes large, _i.e.,_ the BER increases.  
Figure 4: Effects of different SIR on time-domain waveform. (a) EBPSK modulation signal. (b) EBPSK signal after filter (no interference). (c) Interfered EBPSK signal after filter ( \(SIR=10 dB\)). (d) Interfered EBPSK signal after filter ( \(SIR=0 dB\)).  
Figure 6 shows the BER performances in different SIR cases, where the initial phase of the interference is assumed as 0. Obviously, the BERs are almost identical in \(SIR=0 dB\), \(SIR=10 dB\) and no interference, which agrees with our above BER analysis. Different to the normal cases, the BER performance of the EBPSK system does not decrease when the co-channel interference is existing, which implies good anti-interference performance of this high-efficiency EBPSK transmission system.  
Figure 5: The sketch map of the detection performance by pdf.  
Figure 6: BER performances in different SIR.
## 2 Materials and Methods
### Methods  
To evaluate the performance of the PSO-BPNN model, we compared it with the traditional backpropagation neural network (BPNN) and the support vector machine regression model (SVR). After confirming the high retrieval accuracy of the PSO-BPNN model, we applied it to analyze the changes in PC concentration using Sentinel-2 MSI images taken before and after the 2017 red tide outbreak of _P. glo_ in Qinzhou Bay. For a comprehensive understanding, the analysis incorporated additional environmental variables such as sea  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Monitor Point** & **Longitude** & **Latitude** \\ \hline S1 & 108.5483 & 21.7992 \\ S2 & 108.5667 & 21.7312 \\ S3 & 108.6128 & 21.6737 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Latitude and longitude of automatic monitoring stations.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Water Quality** & & & & & \\
**Parameters** & & & & & \\ \hline S1-PC (\(\mathrm{\SIUnitSymbolMicro L}\)) & 4.855 & 0.799 & 2.131 & 1.672 & 1.208 & 20 \\ S2-PC (\(\mathrm{\SIUnitSymbolMicro L}\)) & 4.450 & 1.050 & 2.169 & 2.163 & 0.891 & 18 \\ S3-PC (\(\mathrm{\SIUnitSymbolMicro L}\)) & 4.695 & 1.190 & 2.573 & 2.170 & 1.119 & 17 \\ Total-PC (\(\mathrm{\SIUnitSymbolMicro L}\)) & 4.855 & 0.799 & 2.280 & 2.125 & 1.083 & 55 \\ \hline \hline \end{tabular} The 55 sample data points have been filtered and matched with Sentinel-2 imagery from the years 2015 to 2019.  
\end{table}
Table 3: Statistical analysis of PC concentration.  
surface temperature, precipitation, and inorganic salt concentration. Finally, the indicators for the _P. glo_ red tide outbreak in Qinzhou Bay were established. The technical route of the study is shown in Figure 2.
## 6 Experimental Evaluation and Discussion
### Defense Overhead  
We believe that the overhead of defending WFP is as important as its defensive performance, and the acceptable overhead of defense must be considered when it is actually deployed. Therefore, this part of the experiment is mainly focused on the overhead. We evaluate the time overhead of different defense methods by loading the same pages. In terms of time overhead, SMART has less overhead than other defense methods. We believe this can be due to two reasons. One is that we do not change the transmission time of the original packet and do not add any delay. The other is because multiple cells are transmitted simultaneously over multi-paths, reducing the time overhead of waiting for packets to be queued and sent. After the experimental demonstration, we conclude that SMART has a high defense capability while incurring only a small time overhead, exceeding other previous defense methods. Although the time overhead of SMART is already small, there is still a struggle with zero latency. Next, we will consider redundant coding algorithms with a faster encoding speed and repair speed, such as RRS [49].  
In addition to this, we also deploy SMART in real Tor nodes as a way to evaluate the additional overhead of nodes processing data in a real environment and repeat each set of evaluation experiments 1000 times. As the Tor client needs to do data splitting and redundancy, the middle OR needs to do data recovery and reorganization. We also focus on the time overhead of the user agent nodes and middle OR to process data, as shown in Figure 10. We believe that this part of the time overhead is acceptable and even almost negligible. Since multi-paths may generate jitter phenomena in real networks, we also add a set of comparison experiments to measure packet arrival time for packets transmitted over multiple paths. As shown in Figure 10, multiple connections reduce the packet arrival interval, but the jitter phenomenon generated by some circuits also increases the outliers. Additionally, the multi-path transmission may create the problem of packet loss that cannot be recovered, and we have shown in the above paper that it allows a certain number of packet losses and still restores the complete data.  
Figure 9: Multi-path transmission success rate.  
In summary, we demonstrated the effectiveness of our defense against today's WFP attacks and the transmission reliability during Tor communications, and confirm that this moderate overhead defense can be applied in real anonymity environments as a way to protect the higher privacy of users.
# Optimal Prediction of Individual Vessel Trajectories Based on Sparse Gaussian Processes
## 3 Preliminaries
### Genetic Algorithm  
[PERSON] [36] created the genetic algorithm (GA) as a method that uses a population-based approach to solve optimization problems, regardless of the type of constraint. The GA is both straightforward and resilient, making it applicable even in situations where there is limited understanding of space's properties and attributes. The initial step of the GA involves the random initialization of a predetermined set of individuals. After defining the criteria set by the predetermined objective function, every one is assessed and those that meet the criteria are advanced to the next generation using crossover and mutation operators. This procedure is iterated until the desired assessment outcome or the designated maximum iteration is attained. The selection operation utilized a stochastic uniform selection method described by [PERSON] and [PERSON] [37]. This method involves mapping objects that correspond to a line that is separated by adjacent elements. The size of an individual's distinct section is directly proportional to its fitness. Equidistant pointers were positioned on the line based on the number of selected individuals. At each iteration, the pointer chooses a segment that corresponds to a line in the same position. This approach yields a higher number of qualified children compared to the widely recognized roulette wheel selection method [37]. The crossover operation utilizes the Laplace crossover operator [38] to produce offspring using one of the following equations:  
\[c_{1}=p_{1}+\gamma|p_{1}-p_{2}|, \tag{24}\]  
\[c_{2}=p_{2}+\gamma|p_{1}-p_{2}|, \tag{25}\]  
where \(p_{1}\), \(p_{2}\) are the parents of two offspring \(c_{1}\), \(c_{2}\) and \(\gamma\) is a random number generated from a Laplace distribution.  
A power mutation operator [39] was applied for the mutation operation. The algorithm of the operator mutates parent \(x\), as follows:  
\[y=\begin{cases}x-s(x-x_{l}),\;if\;t<r\\ x+s(x_{u}-x),\;if\;t\geq r^{\prime}\end{cases} \tag{26}\]  
where \(t=\frac{x-x_{l}}{x_{u}-x_{l}}\) and \(x_{u}\) and \(x_{l}\) are the upper and lower bounds of the variable, respectively. \(s\) is a random variable obtained from the power distribution, and \(r\) is a uniformly distributed random number.