esa-sceva/satcom-chunk-collection huggingface dataset:
# A Game-Theoretic Rent-Seeking Framework for Improving Multipath TCP Performance +
## 5 Evaluation
### Additional Experiments
#### 5.5.2 Observation 8  
RSF MPTCP performed well in the face of a dynamically changing number of subflows, and it outperformed the other MPTCP algorithms. When the number of subflows changed over time in the realistic network scenario, we observed a sharp drop in performance at 50 s with all algorithms (including RSF MPTCP). However, our RSF-LB algorithm detected the failed path very quickly. This mechanism made the RSF MPTCP algorithm highly responsive to changes in network paths. The other existing MPTCP algorithms were all quite sluggish in detecting and abandoning the failed path.
## 6 Conclusions  
The vibration control method, a significant concern in the electromagnetic isolation system, can bring controllable stiffness and damping coefficient, and fast response. For improving the performance of an electromagnetic isolation system with reasonable parameters and avoid the parameter tuning problem of a PID controller, an active control method is put forward based on equivalent stiffness and damping coefficient. The equivalent stiffness and damping coefficient are similar to the \(K_{p}\) and \(K_{d}\) parameters of a PID controller. The conclusions are obtained in this paper are as follows:  
1. Due to the two electromagnets with the electromagnetic force could be simplified as a spring or a spring and a damper, the equivalent stiffness coefficient and damping coefficient of the electromagnetic isolation system are calculated by the required range of dynamic performance indexes, which can avoid the parameter tuning problem of a PID controller. According to the established nonlinear relationship, the equivalent stiffness and the equivalent damping coefficient can be satisfied by controlling the coil currents. Based on the model of the system and the proposed active control method, the control diagrams are established for simulations and experiments.
2. The simulation and experimental results verify that the active control approach with the equivalent stiffness and damping coefficient can obtain the desired dynamic performance indexes and control vibration amplitudes for keeping stability of the isolation system.
3. In order to realize the variable control parameters of the electromagnetic vibration isolation system and eliminate overshoot and oscillations, an active control method based on the equivalent stiffness and damping coefficient with the piecewise strategy is proposed. The simulation and experimental results show that the control method based on the piecewise equivalent stiffness and damping coefficient can not only reduce the setting time of the system, but also ensure the stability of the system. The conclusion is useful for vibration control in the active electromagnetic isolation system in practical engineering.  
[PERSON] conceived method; [PERSON] and [PERSON] designed and performed the experiments; [PERSON] analyzed the simulation and experimental data; [PERSON] and [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Basic Research Project of the Knowledge Innovation Program in Shenzhen under Grant JCYJ2017081814449801.  
The authors declare no conflict of interest.  
Figure 9: Experimental results of two active control methods.
# Evaluation of Coastal Sea Level Offshore Hong Kong from Jason-2 Altimetry
## 4 Methodology
### Altimetry Data Processing
#### 4.1.3 Data Editing Strategy  
Near the coast, the number of outliers in data sets is obviously much larger than over the open ocean. Thus the data must be edited before being used. The outliers are detected and removed using the MAD filter. It is based on the median value of the data analyzed, which is more robust than the mean value in the presence of outliers. For a Gaussian random signal, MAD is defined as:  
Figure 5: Sea state bias (SSB) difference with respect to Significant Wave Height.  
\[MAD=\sqrt{\frac{2}{\pi}}\cdot\sigma \tag{3}\]  
where \(\sigma\) is the standard deviation of the data. For the [PERSON]'s criterion (0.5% outliers for a Gaussian series, [31]), it will be appropriate to set a threshold of 2.81 \(\times\)\(\sigma\), equivalent to 3.52 \(\times\)\(MAD\).  
Therefore, values satisfying:  
\[|x-Median|>3.52\times MAD \tag{4}\]  
are defined as outliers. This is very similar to the criterion (3.5 \(\times\)\(MAD\)) adopted by [PERSON] et al. [6] in the ionospheric correction editing process of the X-TRACK product.  
Objectively, all outliers cannot be thoroughly detected by the above criterion. In the presence of distorted waveforms, retrackers such as ALES can occasionally produce unrealistic SLA values, up to tens of meters and hence increase the MAD. Some outliers may remain after editing in this case. So we have defined a threshold value before applying the MAD editing. All the SLA values beyond \(\pm\)2 m are deleted. Some high amplitude real oceanographic features (such as storm surge) may also be deleted. But for the purpose of the present study, the storm surge events can be removed from the analysis. Finally, almost all outliers are detected after this threshold is applied.  
A typical along track profile of 20-Hz SLA before and after editing is shown in Figure 6 for a given cycle (cycle #5 is arbitrarily chosen here). ALES is not as robust as the MLE algorithms for the last few coastal measurements: it has many outliers in almost all cycles (in fact, the mean number of outliers beyond \(\pm\) 10 m in ALES is ~9 per cycle, much larger than for MLE3 or MLE4). However, after editing, ALES performs better than MLE3 and MLE4, since it displays the lowest noise level.
# PCONet: A Trainable Feedback Scheme of Precoder for the Uplink Multi-User MIMO Systems
## 2 System Model
### The Joint Precoding and Detection Design  
In this paper, we consider the scheme in [9] which jointly designs the precoding and detection matrices to minimize the MSE between the estimated and the transmitted symbols. Specifically, we work on the overall system performance with the joint transceiver design which is subjected to the per user power constraint. The sum-MSE can be formulated as  
\[\min_{\{\mathbf{F}_{k}\mathbf{G}_{k}\}\}_{k=1,\ldots,K}\boldsymbol {\eta} \tag{4}\] \[s.t.\quad Tr\{\mathbf{F}_{k}\mathbf{F}_{k}^{H}\}\leqslant p_{k},\,k=1,\ldots,K\]  
where the precoding matrix \(\mathbf{F}_{k}\) is subject to the per user power constraint \(\mathbf{P}_{k}=\mathbb{E}\Big{\{}\|\mathbf{F}_{k}\mathbf{s}_{k}\|^{2}\Big{\}} =tr\{\mathbf{F}_{k}\mathbf{\Phi}_{sk}\mathbf{F}_{k}^{H}\},\forall k\in \mathcal{K}\). The joint minimization of MSE by iteratively updating the transceiver is carried out as follows  
\[\mathbf{G}_{k}=\mathbf{F}_{k}^{H}\mathbf{H}_{k}^{H}\left(\sum_{j=1}^{K} \mathbf{H}_{j}\mathbf{F}_{j}\mathbf{F}_{j}^{H}\mathbf{H}_{j}^{H}+\sigma^{2} \mathbf{I}\right)^{-1} \tag{5}\]  
\[\mathbf{F}_{k}=(\mathbf{H}_{k}^{H}(\sum_{i=1}^{K}\mathbf{G}_{i}^{H}\mathbf{G}_ {i})\mathbf{H}_{k}+\lambda_{k}\mathbf{I})^{-1}\mathbf{H}_{k}^{H}\mathbf{G}_{k} ^{H},\,\,\,\forall k \tag{6}\]  
where \(\lambda_{k}\) is the Lagrange multiplier associated with the user power constraint. The detailed minimization process to obtain the optimal transceiver is described in Algorithm 1. The precoding matrix \(\mathbf{F}\) is initialized with codebooks in the 3 GPP protocol, which is simultaneously normalized to satisfy the power constraints. The Lagrangian formulation is utilized to solve the jointly convex optimization problem. The MMSE detection can optimally balance the multi-user interference and Gaussian noise compared with the ZF detector, Thus, we apply the MMSE detector in the proposed uplink scenario because of its practical implementation and better performance. The output-precoding matrix \(\mathbf{F}_{k}\) and detection matrix \(\mathbf{G}_{k}\) are utilized for precoding and MMSE detection at the \(k\)-th UE and the BS, respectively.  
```
1 Input\(\mathbf{H}_{k}\): the channel matrix;
2\(T\): the maximum number of iterations;
3\(\sigma\): the noise variance;
4 Parameter
5\(t\): the iteration counter;
6\(K\): the number of UEs;
7 Initialize\(\mathbf{F}_{k}\), \(k=1,\ldots,K\) and \(t=0\)
8 while\(t\leqslant T\)do
9 for(\(k=1,\ldots,K\))do
10 compute \(\mathbf{G}_{k}\) with (5)
11 end if
12 for(\(k=1,\ldots,K\))do
13 compute \(\mathbf{F}_{k}\) with (6)
14 end if
15 set \(t=t+1\)
16
17 end for
18\(+\) Output\(\mathbf{F}_{k}\): the precoding matrix;
19\(\mathbf{G}_{k}\): the detection matrix;
```  
**Algorithm 1**The centralized implementation with optimal MMSE precoding.
## 3 Results
### Accuracy of the Model of Variable vs. Constant Albedo  
To assess the model with constant or variable albedo values, _RMSE_, _MAPE_, and \(R^{2}\) were used for \(T_{ae}\) and \(m_{t}\). Figure 12 shows that for \(T_{ae}\), the _RMSE_ and _MAPE_ are lower in most cases for variable albedo in both seasons, with values for _RMSE_ ranging from 3 to 7.5 and below 10% for _MAPE_, while \(R^{2}\) shows a minor increment for variable albedo compared to the constant values. Interestingly for the instantaneous mass, _RMSE_ and _MAPE_ show a more significant improvement of the model outputs when a variable albedo is used. Furthermore, the \(R^{2}\) shows slightly higher values of the model with variable albedo compared to the constant albedo (Table 3). Overall, these results indicate that there is a strong influence of variable albedo in the developed sun drying model.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Season**} & \multirow{2}{*}{**Date**} & \multirow{2}{*}{**MC\({}_{\text{ini}}\)**} & \multirow{2}{*}{**Drying Time**} & \multirow{2}{*}{**Albedo**} & \multirow{2}{*}{\(T_{ae}\)} & \multirow{2}{*}{\(m_{t}\)} \\ \cline{3-4} \cline{6-7}  & & & & & \(R^{2}\) & \(R^{2}\) \\ \cline{3-4} \cline{6-7}  & & & & & \(-\) & \(-\) \\ \hline \multirow{8}{*}{Rainy} & 12.10.2011 & 0.3363 & 52.0 & C & 0.6396 & 0.2029 \\  & & & & V & 0.6114 & 0.2182 \\  & 19.10.2011 & 0.2490 & 52.3 & C & 0.8363 & 0.8952 \\  & & & & V & 0.8161 & 0.9049 \\  & 25.10.2011 & 0.2909 & 76.7 & C & 0.6562 & 0.8524 \\  & 14.11.2012 & 0.3810 & 43.0 & C & 0.8199 & 0.8611 \\  & 19.11.2012 & 0.3530 & 54.4 & V & 0.8729 & 0.8160 \\  & 22.11.2012 & 0.2578 & 49.5 & C & 0.8777 & 0.8491 \\  & 22.05.2012 & 0.2431 & 26.0 & C & 0.8012 & 0.6700 \\  & 29.05.2012 & 0.2441 & 30.0 & C & 0.8015 & 0.7000 \\  & 04.06.2012 & 0.2427 & 33.0 & C & 0.8725 & 0.8426 \\  & 09.04.2013 & 0.1897 & 29.5 & V & 0.8818 & 0.8490 \\  & 11.04.2013 & 0.1680 & 3.5 & C & 0.8062 & 0.9328 \\  & 16.04.2013 & 0.2589 & 29.5 & V & 0.7471 & 0.9405 \\  & & & & C & 0.9246 & 0.9847 \\  & 11.04.2013 & 0.1680 & 3.5 & V & 0.9304 & 0.9859 \\  & 16.04.2013 & 0.2589 & 29.5 & C & 0.7682 & 0.9692 \\  & & & & V & 0.7611 & 0.9571 \\ \hline \end{tabular}
\end{table}
Table 3: Coefficient of determination \(R^{2}\) of air temperatures \(T_{ae}\) and instantaneous mass \(m_{t}\) at a constant albedo (C) of \(\alpha_{pu}=0.1957\) and variable (V) albedo \(\alpha_{pa}\).
# On the Track to Application Architectures in Public Transport Service Companies
## 4 Current State of Use-Cases from Different Transportation Service Providers
### AI in Rail-Inspect at SBB  
SBB is an early adopter when it comes to the application of AI. SBB's goals are to increase the safety of its railways infrastructure, reduce costs, enable new business models and cope with the ever-growing transportation demand. SBB has different use-cases ranging including supervised ML, reinforcement and unsupervised learning, knowledge representation or optimization. Some of them are described on the flatland-challenge website [21]. Below we will provide a summary of one of the flagship cases, called AISI (AI  
Figure 5: Arc ignitions in tunnels (**a**,**b**,**d**,**e**,**g**) and in open air (**c**,**f**).  
in Railwaysinspect), which is the first major operational project at SBB that makes massive use of deep learning technologies and can be classified as a TRL9 application.  
We also summarize the lessons learned both during implementation and operation. The project AISI was initiated with the aim to automate the track inspection and to close the gap between demand and availability of resources. The goal of AISI is to analyze image data with the objectives to increase the network safety and to reduce the overhead for track inspection. It uses state-of-the-art Deep Learning methods to detect infrastructure defects from high resolution images captured by line-scan cameras of diagnosis vehicles. Five parallel installed line-scan cameras capture detailed images from the tracks and transfer them to the AISI ML system for further analysis. The defects found are market with bounding boxes and categorized by the type of defects, as depicted in Figure 6 below.  
The first ML-model has been put into production but covers only a subset of the 250 different known defect types. However, it achieves much higher detection rates compared to previous non-ML algorithms. AISI was started in 2017 as a research prototype showing promising results. However, the path from a research prototype on TRL4 to an enterprise ready product on TRL9 is very elaborate and expensive, especially for products that have to incorporate many other aspects apart from the yet complex model development. The following sections describe the main challenges to integrate ML components into operational processes and propose possible approaches to tackle them.
## 3 Multipath Parameter Estimate Algorithm
### Effect of Window Length \(L\)  
The window length \(L\) is of crucial importance to the proposed method. It not only affects the computation amount as we mentioned above, but also has direct effect on other two aspects: the robustness to the noise and maximum detectable multipath delay of the proposed method. By analyzing the effect of \(L\), we can have a better understanding of the new technique and apply it properly under various noise levels.  
As mentioned above, the number of segments \(M\) is a function of \(L\). For a given overlap rate \(r\), a smaller \(L\) means a larger \(M\). Then the noise will be smoothed for more times and the robustness of the method is thus improved accordingly.  
It should be noted that the robustness improvement comes along with the decrease of maximum detectable multipath delay. The maximum detectable multipath delay \(\tau_{\max}\) is defined as the maximum range of the multipath delay that the method is able to detect. It can be expressed by:  
\[\tau_{\max}=L/f_{s}T_{c} \tag{25}\]  
So for a multipath component with a code delay of \(\tau_{\Delta}\) in units of code chips, we need to make sure \(\tau_{\Delta}<\tau_{\max}\) to keep it detectable. Generally, this requirement is easy to satisfy. The range of multipath delay is typically varying from 0 to 2 chips, because if the path delay is too large (such as larger than 2 chips), this ray will suffer from severe amplitude attenuation and thus have little impact on the positioning accuracy of user receivers. Considering the worst circumstance that \(\tau_{\Delta}\) is 2 chips and the sampling rate \(f_{s}\) is 20 MHz, as long as the \(L\) is larger than 40 points, \(\tau_{\max}\) will exceed 2 chips and this reflected ray can be detected.
# Improving Structural Power Content Analysis Robustness for Satellite Navigation Applications +
## 2 Structural Power Content Analysis
### Test Statistic and Threshold Setting  
For the purpose of defining a test statistic to determine the presence or absence of a spoofing signal, the output of the normalized signal filter is modeled as a normally distributed random variable. This is justified by the central limit theorem and the fact that the received signal comprises the superposition of multiple independent PRNs, in addition to thermal noise. In the absence of spoofing, the variance in \(x[n]\) is the sum of thermal noise and the variances of the independent PRNs transmitted from visible GNSS satellites. When spoofing is present, the variance increases as additional PRN signals transmitted by the spoofer are received. Depending on the nature of the spoofing attack--distinct PRNs, the same PRNs as visible satellites but with a different code phase, or the same PRNs with a matching code phase--the level of increase in variance can vary.  
A generalized likelihood ratio test (GLRT) detector is then defined to distinguish between the null hypothesis (spoofing absent) and the alternative hypothesis (spoofing present) based on an estimate of the variance in \(x[n]\):  
\[T(x)=\frac{1}{M}\sum_{m=1}^{M}x[m]x[m]^{*}, \tag{4}\]  
\[\left(\begin{array}{c}H_{0}\ :\ T(x)\ \leq\ \gamma\\ H_{1}\ :\ T(x)>\gamma,\end{array}\right) \tag{5}\]  
where the threshold is determined empirically for a desired probability of false alarm. Specifically, the sample mean \(\mu_{0}\) and variance \(\sigma_{0}^{2}\) of the test statistic are computed over many epochs under conditions that are known to be free of spoofing. Under the assumption of normal distribution for the test statistic, the threshold is then calculated as follows:  
\[\gamma=\mu_{0}+\sqrt{\frac{\sigma_{0}^{2}}{N}}\ Q^{-1}(P_{FA}), \tag{6}\]where \(Q^{-1}(P_{FA})\) is the Q-function (the inverse of the right-tail distribution) evaluated at the desired false alarm rate. It is noted that, in general, the SPCA technique does not require the calibration of the spoofing detection threshold [6]; however, the filtering techniques proposed below generally do not result in equivalent bandwidths for the signal and noise filters. For this reason, the threshold is determined empirically here, although proper normalization for a particular choice of filters can be derived by (numerically) calculating their effective bandwidths.  
To summarize, the SPCA method is based on concentrating the power of GNSS signals, both legitimate and spoofed, in the spectral domain. Sudden increases in this concentrated power are then used to detect spoofing attacks. However, certain jamming (or more general unintentional interference) signals can produce spectral distributions in the DAM operation's output that yield power increases like those associated with spoofing signals and can therefore increase the false alarm rate for the SPCA method as a spoofing detection algorithm. In the next section, different jammer models are presented, and it is shown how certain jamming signals can be falsely identified as spoofing.
# Metabolic Rate Measuring with Indirect Calorimetry for Thermal Comfort Evaluation
###### Abstract  
The metabolic rate (MET) is a fundamental parameter influencing thermal comfort. In this paper, the MET is obtained by indirect calorimetry to propose equations and related laws applicable to the environment at an altitude of 4 m, so as to enrich the research. Here, experiments with 30 healthy male subjects are conducted at sitting, 2, 4, and 6 km/h walking speeds in various ambient environments controlled by the climate chamber. The MET, thermal sensation vote (TSV), and heart rate (HR) were collected during the experiments. The results indicate that the ambient environments have little impact on MET, but it is obvious for HR. The linear relationship between walking speed, HR, and MET is fitted to be MET = 28.38 \(\times\) Speed + 62.25 and MET = 3.67 \(\times\) HR \(-\) 195.56. Moreover, the TSV and five kinds of predicted mean vote (PMV) calculated by various sources of MET are compared. For sitting activity, a slight difference from the MET could cause a significant difference in PMV, which leads to the PMV that may be above, below, or close to the TSV. For the 2 km/h walking activity, five kinds of PMV are close to TSV. However, for the 4 km/h walking activity, PMV is always higher than TSV. These findings are significant in elucidating the relationship between MET and thermal comfort.  
indirect calorimetry; metabolic rate; ambient environment; thermal sensation
# Joint Optimization of Data Transmission and Energy Harvesting in Relay Satellite Networks
## 4 Solutions and Algorithms
### Decomposition Strategy  
As shown in (17), we transform the optimization problem of minimizing \(\Gamma_{V}(t)\) to three sub-problems: battery management optimization (BMO), data acquisition control optimization (DACO), and link transmission optimization (LTO), which are optimized separately.
## 3 The Lossless Adaptive Data Compression Method Based on the Binomial Number System (BNS)
### Methods for Converting Binomial Code to Binary  
Converting binomial numbers to binary numbers is essential to the described compression method. It is implemented using the binary binomial number system [23,24].  
**Definition 1**.: _A binary \(k\)--BNS is a system that includes a numeric function:_  
\[F=x_{r-1}C_{n-1}^{k-q_{r-1}}+\ldots x_{i}C_{n-r+i}^{k-q_{i}}+\ldots+x_{1}C_{n- r+1}^{k-q_{1}}+x_{0}C_{n-r}^{k-q_{0}}\,, \tag{1}\]  
_set of binomial numbers \(F=x_{r-1}\ldots x_{i}+\ldots+x_{1}x_{0}\), binary alphabet \(x_{i}=0,1\), and systems of conditions, generating binomial code [23,24]:_  
\[q\leq r\leq n-1, \tag{2}\]  
\[x_{0}=1,\,q=k, \tag{3}\]  
_and_  
\[n-k=r-q, \tag{4}\]  
\[x_{0}=0,\,0\leq q\leq k-1\,, \tag{5}\]  
_where \(r\)--the number of binary digits in binomial numbers (code length); \(r\in 1,2,\ldots\). \(i\)--is a position index \(i=0\), \(1\), \(\ldots\), \(r-1\); \(q\)--is a number of \(1\)'s in a binomial code; \(n\), \(k\)--is an integer parameter of the BNS \(-\) 1,2, \(\ldots\) ; \(q_{i}\)--is a sum of \(1\)'s bits of the \(x_{i}\) from (\(r-1\))-th to the (\(i+1\))-th bits,_  
\[q_{i}=\sum_{j=i+1}^{r}x_{j} \tag{6}\]  
\(i=0,1,\ldots,r-1;x_{r}=0\).  
Step 2 (see Figure 2) is an essential part of this method, converting the binomial number using the binomial numeric function to the number of the natural positional number system.  
Figure 2: The adaptive binary sequence compression method.  
Since the binomial coefficient determines the number of binomial numbers, \(C_{n}^{k}=n!/k!(n-1)!\) it will be equal \(C_{6}^{4}=15\) in the example. A feature of binomial numbers is that they have a variable code length \(m\), varying from \(k\) or \(n-k\) and up to \(n-1\) bit, and have either \(k\) units or \(n-k\) zeros in their composition. Since in the example \(k>n-k\), the minimum length of binomial numbers in it is \(n-k=2\) bits, and the maximum \(n-1=6-1=5\) bits. Accordingly, in the end, they contain either 0, which will be \((n-k)\) in a row or 1, which will be \(k\) in a row.