esa-sceva/satcom-chunk-collection huggingface dataset:
# On the Use of Bright Scatterers for Monitoring Doppler, Dual-Polarization Weather Radars
## 2 Materials and Methods
### The Dual-Polarization, Doppler Radar Located on Monte Lema at an Altitude of 1625 m
#### 2.2.5 The Copolar Correlation Coefficient  
Another quantity measured by the dual-polarization radar is the correlation between the copolar horizontal, \(HH\), and the vertical, \(VV\), returns, which is called the copolar correlation coefficient (often referred to as \(\rho^{HV}\) or \(\rho^{\alpha}\)). However, describing the rather complicated nature of this variable is beyond the scope of this note. On the one hand, any reader that is interested may refer to the electronic supplement (e06.1) that accompanies the book by [PERSON] (1954); on the other hand, the detailed treatment of the physics and use of polarimetric quantities has been the subject of entire books, e.g., [PERSON] (1955). The copolar correlation coefficient is the module of the complex correlation coefficient between two orthogonal components (represented by two complex numbers) of the backscattered electromagnetic field within the radar sampling volume: hence, it ranges from 0 (no correlation between the two polarizations) to 1 (perfect correlation). In general, if the sampling volume contains a significant number of ground targets, then, in most cases, \(\rho^{HV}\) ranges from 0.75 to 0.95, for example. However, for the bright scatterer in Cimetta that will deal with in the Section 3, the median of the five daily values is as large as 0.9972. A maximum value of 0.9998 has been observed.  
Regarding the 1-byte quantization, a considerable resolution is attributed (through a logarithmic transformation) to values of \(\rho^{HV}\) close to 1; hence, low values of \(\rho^{HV}\) are compressed into a few Digital Numbers. The corresponding conversion formula from DN to \(\rho^{HV}\) is:  
\[\rho^{\rm HV}=1.003-\left(10^{-\frac{DN-1}{100}}\ \right). \tag{6}\]  
DN = 0 corresponds to \(\rho^{HV}\) = 0, while DN = 255 is obviously not possible.
# A Mathematical Model for Efficient and Fair Resource Assignment in Multipath Transport
## 5 Performance Evaluation
Exceptions are the scenarios 23, 25 and 26 where BSF and BFF yield a value which is slightly greater than 1. This means that the BSF and BFF methods are better by a small amount in this case than NFF against which the BSF and BFF performance is normalized. The topologies deployed in these scenarios include a single link which connects two subnetworks. Bottleneck-based fairness methods appear to be better able to cope with such a situation. Figures 22-24 show the results for the fairness metrics for bottleneck subflow fair and bottleneck flow fair, again normalized by the metrics for network flow fair to provide comparability between the different scenarios. Most values range between 0.8 and 1 for BSF and BFF, which means that NFF, which is always one by definition, achieves better fairness. A direct comparison between the different BSF and BFF methods shows however no significant difference. There is a slight trend that fairness degrades in the case of scenarios 29 to 31 which are all based on the complex meshed network topology shown in Figure 20, with different flow sets. Since this is one of the most complex scenarios which was investigated, it means that realizing fairness for these more complex setups is more difficult than for simpler ones. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scenario** & \(\mathbf{AIIoc}\) & \(\mathbf{\gamma_{\text{Jain}}}\) & \(\mathbf{\gamma_{\text{Vard}}}\) & \(\mathbf{AIIoc\cdot\gamma_{\text{Jain}}}\) \\ \hline  
4 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Performance evaluation for BFF-II.\(Alloc\) values in Mbps. The examples show how optimum resource allocation and fairness can be achieved in case of optimum conditions, i.e., perfect global knowledge of the network topology and of all flow demands which are injected at different nodes. Practical congestion control methods for multipath transport are restricted to observations at the end nodes, e.g., an increase of the delay or packet losses. With this limited knowledge, they have to draw conclusions about the conditions in the network which obviously cannot be optimum. The theoretical methods can however serve as a benchmark to show how well a practical multipath transport implementation can fulfill performance and fairness requirements, in other words, how closely it approaches the optimum. \begin{table}
\begin{tabular}{c c c c c} \hline \hline scenario & \(Alloc\) & \(\gamma_{\text{Jain}}\) & \(\gamma_{\text{Vard}}\) & \(Alloc\cdot\gamma_{\text{Jain}}\) \\ \hline  
\hline \hline \end{tabular}
\end{table} 
Table 14: Performance evaluation for NEF. \(Alloc\) values in Mbps. Figure 21: Aggregate allocation, normalized by NFF, for different fair allocation methods, based on the numerical results in Tables 10–14.
# A Survey on Optimal Channel Estimation Methods for RIS-Aided Communication Systems
###### Abstract
\(b\sim\mathcal{CN}(0,\Sigma)\) is the symbol for a circularly symmetric complex Gaussian vector with zero mean and covariance matrix \(\Sigma,\lambda\) is the wavelength, \(\omega\) is the discount factor, \(S\) is the finite set of states, \(A\) is the finite set of actions, \(\theta\) is the vector of policy parameters and \(\theta_{old}\) denotes the vector of policy parameters before the update; \(\tau_{\theta}\) is the policy neural network with parameters denoted by \(\theta\) and \(\pi:S\times A\rightarrow\left[0,1\right]\), \(a_{t}\) and \(s_{t}\) are the action and state of given time step t, respectively, with \(a_{t}\sim\pi_{\theta}(a_{t}|s_{t})\), \(r:S\to R\) is the reward function, \(\varphi_{n}\) is the phase shift introduced by the \(n\)-th element of the RIS, \(v\) is the velocity of the user, \(c=3\times 10^{8}\) m/s is the speed of light and \(f_{c}\) is the carrier frequency. Finally, \(I_{M}\) stands for the \(M\times M\) identity matrix.
## 5 Performance Evaluation
### Simulation Results
#### 5.3.3 End-To-End Delay  
The hot spot ratio also affects the end-to-end delay because the processing delay will increase on a specific node as the number of available paths decreases. In this simulation, we set the data processing rate as 15,000 bits per second considering the limited hardware performance of WBAN devices.  
As shown in Figure 6a, the end-to-end delay is higher as the traffic bit rate increases. MTARP shows the best performance since it has the highest weight of hop count, however, the performance of two protocols does not reveal great difference. The reason is related to the hot spot ratio. Since the MTARP tries to forward the data packet to the node with lower hop count, the node becomes a hot spot quickly. Accordingly, as the number of available routing paths decreases, the load distribution will not be achieved efficiently. Therefore, the data processing delay gradually increases as the traffic bit rate increases. Figure 6c shows the performance change with hot spot ratio. It should be noted that the performance difference of the comparative protocols is not large when the hot spot ratio is the same. The reason is that, even though the weight of hop count of MTARP is higher than other protocols, the load distribution over temperature is not good.  
The hello packet interval also affects the end-to-end delay. Figure 6b shows that when the hello packet interval is set low, the processing delay increases because the hello packet is transmitted frequently. However, if the hello packet interval is set high, the end-to-end delay due to retransmission increases because the position change of the node cannot be recognized quickly.  
Figure 5: Performance comparison for difference traffic bit rate, hello packet interval and hot spot ratio. (**a**) Impact of varying traffic bit rate on PDR, (**b**) Impact of varying hello packet interval on PDR, (**c**) Impact of hot spot ratio on PDR.  
In conclusion, we can reduce the end-to-end delay by increasing the weight of hop count metric. However, the actual performance is not great because the data processing delay will increase when the traffic load is concentrated on the node with lower hop count. In addition, additional performance degradation occurs when the hot spot ratio increases because it is hard to perform load distribution as the number of available routing paths decreases. Therefore, we have to consider the temperature rise of the node more significant than the hop count metric.
# Optimal Progressive Pitch for OneWeb Constellation with Seamless Coverage
## 3 Algorithm Design
### Genetic Operator Design
#### 3.5.3 Selection  
The selection operation selects good chromosomes from the old population in a certain way to form a new population in order to reproduce and get the next generation of the population. In this paper, we select the top twenty percent of individuals from the population in terms of fitness and then sample the selected individuals to obtain a new population. Therefore, the new population is generally better than the parent population regarding fitness.
# Postharvest Evaluations of Blackcurrant Fruits with Chitosan and Ultraviolet A Treatments
## 1 Introduction
UVA (315-400 nm), as one of the longest wavelengths among these three types of UV radiation, had less attention from the postharvest researcher, although some have reported that UVA also had effects on reducing microbial populations, changing colors, and stimulating biomass production. UVA and UVB have been reported to modify grape berries' overall quality traits and monoterenes [22]. UVA stimulation on fruits can be used to improve fruit quality and alleviate flavor loss of postharvest fruits during shelf life [23]. An earlier study of blackcurrant postharvest research with imaging technology indicated that room temperature-stored blackcurrant fruit had visibly greater shifts in quality than refrigerated temperature-stored blackcurrant fruit [24]. Research on commercial blackcurrant cultivars in Russia indicated that blackcurrant fruit stored at room temperature could not last more than one day when considering the quality criteria of firmness, color, and tastes [25]. In this study, we built on this, utilizing the refrigerated temperature for testing the postharvest treatment of blackcurrant fruit with supplemental light and chitosan coatings. Although there have been some postharvest studies on blackcurrants, none of them have applied chitosan with light radiation together. Here, we pioneered postharvest research with the utilization of both technologies and tested single or combined effectiveness with the two treatments. This study aimed to describe the effects of chitosan, UVA, and combined treatments on postharvest blackcurrant quality.
## 3 Review of Methods
### Stacked Autoencoders  
For a variety of applications, dense or compressed representations of input data using unlabeled data are required to reduce dimensionality by automatically extracting significant features. _Autoencoders_ and modifications of them have proven their suitability across a variety of fields, especially in the image-processing domain. A basic autoencoder (AE) consists of an _encoder_, which generates a _latent representation_ of the input data in one hidden layer of much lower dimensionality (_codings_), and a _decoder_, which reconstructs the inputs based on these codings. By using _Stacked Autoencoders_ (_SAEs_) that have multiple symmetrically distributed hidden layers (_stacking_), the capability to handle inputs that require complex codings can be extended (see Figure 8).  
[PERSON] et al. [8] used an SAE for feature extraction and a softmax regression classifier for fall detection. Among the positive effects of the proposed preprocessing method, an accuracy of 87% was achieved.  
[PERSON] et al. [41] used an SAE, in addition to an SVM and a CNN, to evaluate performance using multidomain features, i.e., range-time (_RT_), Doppler-time (_DT_), and cadence velocity diagram (_CVD_) maps, based on an open dataset [35] and an additional dataset. It was shown that for different feature fusions, the CNN was the most robust method, followed by the SAE and the SVM.
## 2 Materials and Methods
### Machine Learning Methods
#### 2.2.2 Logistic Regression  
Logistic regression enables determining the nonlinear relationship between the dependent variable and independent variables with two or more categories. It is a method that estimates the probability of occurrence of one of the values the dependent variable can take according to the model created. By making logarithmic transformations, the relationship is transformed into a linear structure, and the probability of certain outcomes for each observation is specified [26].  
Since logistic regression is based on probability, the model result obtained should also be in the range of 0 to 1. Probability in logistic regression is expressed as \"odds\". Odds are expressed as the ratio of the probability of an event occurring to the probability of it not occurring. The formula is:  
\[\text{Odds}=\frac{\text{P(x)}}{1-\text{P(x)}} \tag{8}\]  
Here, P(x) represents the probability of an event occurring, and \(1-\text{P(x)}\) represents the probability that the event will not occur. The odds ratio ensures that the probability estimation takes a value between 0 and 1, but it should also be ensured that the odds do not take a value below zero. For this, it is necessary to calculate the logit value, which expresses the logarithm of the odds [27].  
\[\text{ln}\,\frac{\text{P}}{1-\text{P}}=\beta_{0}+\beta_{1}\text{x}_{1}+\dots+ \beta_{k}\text{x}_{ki} \tag{9}\]  
The logit is negative if the odds are less than 1 and positive if they are larger. The resulting model (the intended model) gathered as a result of logistic regression analysis is a nonlinear logarithmic function, and the coefficients obtained are shown as logarithmic values. Therefore, it is difficult to interpret the coefficients. To interpret these coefficients meaningfully, the odds ratio, that is, the Exponentiated logistic coefficients, is used [28].  
\[\text{ln}\,\frac{\text{P}}{1-\text{P}}=\text{e}^{(\beta_{0}+\beta_{1}\text{x} _{1})} \tag{10}\]
# A Novel Dual-Wavelength Method for Evaluating Temperature Effect in Fiber-Optic SPR Sensors
## 5 Conclusions  
In this paper, we analyze the temperature effect on the performance of fiber-optic SPR sensors in theory. Based on theoretical analysis, a novel dual-wavelength method is presented to simultaneously measure the changes of RI and temperature. The detailed theoretical calculation is carried out to analyze the temperature effects on the performance of the SPR-based fiber-optic sensor. We then experimentally demonstrate the effectiveness and capability of the proposed dual-wavelength technique. The proof-of-concept experiment arrangement is as simple and robust as the conventional single-channel fiber-optic SPR sensor. The dual-wavelength technique is practical and convenient for various applications, which makes full use of the signals at two fixed wavelengths without need of extra sensing structure design. With the dual-wavelength technique, the RI change can be distinguished from the temperature interference. Therefore, the proposed dual-wavelength technique may further extend the potential of the fiber-optic SPR sensor and pave a way to optimize the performance against temperature interference.  
Conceptualization, L.W., R.W., W.L.; methodology, N.S., L.W., Z.Z., W.L.; software, N.S., L.W.; validation, N.S., L.W.; formal analysis, N.S., Z.Z., L.W.; investigation, N.S., Z.Z.; resources, L.W., R.W.; data curation, N.S., L.W.; writing--original draft preparation, N.S., W.L., L.W.; writing--review and editing, N.S., L.W., W.L.; visualization, N.S., L.W., W.L.; supervision, R.W., W.L.; project administration, R.W.; funding acquisition, Z.Z., R.W., L.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the major scientific and technological innovation project of Shandong Province, China (2019 JZZY010730) and major scientific and technological project of Ningxia, China (nxxdjxm2016-04-01).  
Not applicable.  
This research was funded by the major scientific and technological innovation project of Shandong Province, China (2019 JZZY010730) and major scientific and technological project of Ningxia, China (nxxdjxm2016-04-01).
## 2 The Proposed Method  
The suggested method Velody 2 consists of encoding encrypted data at high capacity into the velocities of the note-on events, while mimicking humanization available in tools with MIDI support such as Ableton Live [16]. The source code of the proposed method is publicly available at [[http://github.com/wecksten/Velody-2.0](http://github.com/wecksten/Velody-2.0)]([http://github.com/wecksten/Velody-2.0](http://github.com/wecksten/Velody-2.0)) as referred to in Appendix A. It achieves the properties of being blind, high capacity and provides steganalysis resilience of data embedding.  
The effect from using this method is that it sets the velocities to values within a narrow interval to be specified, thus removing potential mood swings in the velocity parameter of the music. This effect is minimized by restricting the use of it for organ and harpsichord music (which naturally is performed with close to constant velocities). Therefore, suspicion from steganalysts is avoided regarding the audible change of velocities. Nevertheless, the method can be used with little artificial effect on a wider range of music, as indicated by including the piano piece Fur Elise by [PERSON] in the set of MIDI songs in the experiment to test for audibility effects of the method. Of course, it is not restricted to single-instrument music, but restricting it to organ and harpsichord merely means requiring that the steganography is performed only on the velocities of these instruments though they could be a part of a larger ensemble. An example of music for an ensemble with multiple instruments is Cantata Cantata Gott der Herr ist Sonn und Schild by [PERSON] which was part of this study.  
Regarding the property of performance preservation, using Velody 2 for steganography of organ and harpsichord music should not change performance to any extent that leads to suspicions from steganalysis. As for the property of reversibility, if the point with this is to be able to show a MIDI file which does not contain any hidden message once having extracted it, this can be achieved in other ways. Therefore, this property is regarded as less important compared to the properties of steganalysis resilience and capacity for instance.  
To embed a plaintext message in a carrier MIDI file the process can be split up into three steps: (1) data preparation, (2) data encryption, and (3) data encoding. The extraction process of a plaintext message from a stego MIDI file is performed in a very similar manner by reversing the order of the steps (4) data decoding, (5) data decryption, and (6) data unboxing.
# Design and Performance Analysis of Navigation Signal Based on OFDM
## 5 Performance Evaluation of OFDM Navigation Signals
### Anti Interference Ability  
#### 5.5.1 Spectral Separation Coefficients  
The spectral separation coefficient (SSC) measures the interference caused by unintended signals and can reflect the compatibility between different signals [30]. Table 1 shows the SSCs of several different navigation signals when the bandwidth of the receiver is 36 MHz. The SSCs between two identical signals represents intra-system interference, and SSCs between different signals represents the interference between different systems. We find that by selecting the appropriate parameters, the OFDM navigation signal and the existing navigation signal have a smaller SSC, and the SSC is below \(-\)73 dB, therefore it has better spectrum isolation and lower interference to the existing navigation signal.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ksec(dB/Hz)** & **BPSK-R(10)** & **BOC(1,1)** & **BOC(10,5)** & **OFDM(128)** & **OFDM(8)** \\ \hline BPSK-R(10) & \(-\)71.86 & \(-\)70.89 & \(-\)87.06 & \(-\)73.36 & \(-\)74.60 \\ \hline BOC(1,1) & \(-\)70.89 & \(-\)66.12 & \(-\)87.03 & \(-\)73.09 & \(-\)75.11 \\ \hline BOC(10,5) & \(-\)87.06 & \(-\)87.03 & \(-\)73.59 & \(-\)79.23 & \(-\)76.88 \\ \hline OFDM(128) & \(-\)73.36 & \(-\)73.09 & \(-\)79.23 & \(-\)72.85 & \(-\)73.53 \\ \hline OFDM(8) & \(-\)74.60 & \(-\)75.29 & \(-\)76.87 & \(-\)73.53 & \(-\)73.33 \\ \hline Self & \(-\)71.86 & \(-\)66.12 & \(-\)73.59 & \(-\)72.85 & \(-\)73.32 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The SSCs of several navigation signals.  
Figure 12: Code tracking ring S-curve.
# Effect of Temperature on the Modal Variability in Short-Span Concrete Bridges
## 3 Results
### Analysis of the Effect of Temperature  
Figure 8 plots the natural frequencies obtained through modal identification together with the temperature according to date. The indicated temperatures are the hourly averages. Some data are missing because measurement could not be made due to the interruption of power supply by the solar panels and auxiliary batteries in winter. The measured temperatures were low around January 2022 and showed similarly high values in April 2022. The lowest temperature was about \(-7\)\({}^{\circ}\)C, and the highest one approached 23 \({}^{\circ}\)C. The identified modes exhibited a pattern where the natural frequencies decreased with higher temperatures.  
Figure 9 plots the identified modes with respect to the temperature in order to examine clearly the effect of temperature on the modes. The tendency of the natural frequencies to decrease with increasing temperatures can be clearly distinguished. The modes of the data measured in 2021 are indicated by \(\cdot\), and those of the data measured in 2022 are indicated by x. The modes of 2021 and 2022 exhibit similar frequencies at identical temperatures. Consequently, it can be concluded that the two bridges did not suffer particular damage during the measurement period. The changes in the natural frequencies of each mode can thus be attributed to environmental factors such as temperature rather than to the occurrence of damage in the bridges.  
Figure 8: Daily variation patterns of natural frequencies: (**a**) Seolmun Bridge; (**b**) Gajwa Bridge.  
Figure 9: Variation patterns of natural frequencies according to temperature (2021 data indicated by dot(\(\cdot\)), 2022 data indicated by x): (**a**) Seolmun Bridge; (**b**) Gajwa Bridge.  
The plain lines in Figure 9 are the regression lines for the natural frequencies of each mode in relation with the temperature. The regression lines for the modes of Seolmun Bridge and Gajwa Bridge are expressed in Equations (1) and (2), respectively.  
\[\begin{array}{l}\overline{f}_{1}=-0.0061\ T+6.0458\\ \overline{f}_{2}=-0.0154\ T+7.9797\\ \overline{f}_{3}=-0.0180\ T+9.6538\\ \overline{f}_{4}=-0.0290\ T+11.7438\\ \overline{f}_{1}=-0.0057\ T+8.3631\\ \overline{f}_{2}=-0.0106\ T+10.3632\\ \overline{f}_{3}=-0.0229\ T+12.5218\\ \overline{f}_{4}=-0.0106\ T+14.6219\end{array} \tag{1}\]  
where \(\overline{f}_{i}\) is the natural frequency of the \(i\)-th temperature-dependent mode (in Hz); and \(T\) is the temperature (in \({}^{\circ}\)C). It appears that the natural frequency decreases at a rate of -0.0290 to -0.0057 Hz/\({}^{\circ}\)C with increasing temperature. Comparing the two bridge types reveals that the RC slab bridge has higher temperature dependency than the RC rahmen bridge.