esa-sceva/satcom-chunk-collection huggingface dataset:
# Collaborative Mobile Robotics for Semantic Mapping: A Survey
## 4 Open Problems and Ongoing Trends
### Collaborative Semantic Mapping of Indoor Environments  
While many works on single-robot semantic mapping focus on indoor environments, the few recent works that address collaborative semantic mapping focus primarily on outdoor environments. Indeed, in these environments, the area to be mapped is large and using a fleet of robots has many potential advantages, such as distributing tasks to reduce work time and obtaining a more accurate representation of the environment. For indoor environments, work in the literature focuses on the semantic mapping of office and domestic environments to facilitate the integration of robots into these environments and extend their capabilities. In these settings, a single service robot is typically used, so the collaborative approach is not very interesting, but in other indoor environments that could be explored in future work, there may be benefits. For example, in industrial environments, where high precision is required, a collaborative approach can provide a more accurate representation of the environment.
# Pushover Analysis on Infill Effects on the Failure Pattern of Reinforced Concrete Frames
## 3 Results and Discussions
### Bending Moments of Columns  
According to the Chinese code [15], 2% story drift can be defined as strong earthquakes. Thus, the bending moments of columns are calculated at 2% story drift and are compared in Figure 12. Figure 12 shows that the infills affect the bending moment of columns significantly, especially for the half-infilled and full-filled frames. The height of the inflection point within the column moved to 0.89 h (Column 5 of axis C) and 0.85 h (Column 1 of axis A) from the column feet borders, respectively. In the inflection point method, the inflection point is at 2/3 h from the column feet for the 1 st floor columns (where H is the clear height of the column). Table 3 summarizes the comparison between calculation results by codes and FEM analysis. The values of the column moments are calculated by the inflection point method. Some FEM analysis results indicate that by considering the actual infill contribution, the bending moments could be 6.1%-30.9% larger than those according to the design codes. It could be dangerous, according to the standard design approach. Therefore, infills should be paid sufficient attention in seismic design.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**The Maximum Column Shear Force /kN**} \\ \hline \multirow{2}{*}{**Location**} & \multicolumn{2}{c}{**Axis A (Half-Filled Infilled Wall)**} & \multicolumn{2}{c}{**Axis B (Non-Infilled Wall)**} & \multicolumn{2}{c}{**Axis C (Full-Filled Infilled Wall)**} \\ \cline{2-7}  & **Column 1** & **Column 2** & **Column 3** & **Column 4** & **Column 5** & **Column 6** \\ \hline Calculated by Equations (11)–(14) & 27.7 & 49.4 & 6.3 & 10.1 & 71.0 & 50.2 \\ Chinese code [15] and AC1 318-14 [27] & 10.8 & 13.3 & 6.3 & 10.1 & 11.8 & 9.9 \\ ECS [13] & 15.2 & 18.5 & 6.3 & 10.1 & 11.8 & 9.9 \\ ASCE41-06 [14] & 21.6 & 26.6 & 6.3 & 10.1 & 12.8 & 10.7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The calculated maximum column shear force.  
Figure 11: Base shear distribution of each axis frame.
# A Symbolic Encapsulation Point as Tool for 5G Wideband Channel Cross-Layer Modeling
## 5 Numerical and Simulation Results  
### HST Scenario  
The scenario in which a communication link between a fixed base station and a fast-moving receiver is emulating the transition of a high-speed train, moving up to 500 km/h and causing channel conditions to change rapidly. In order to do our simulation setup, we choose a lower 5G frequency range (up to 7125 MHz), starting from a reference point of 3500 MHz. Looking at (10), computation of the appropriate distance matrix (BS-Scatterer-MS, for each sample point) is done. For matter of convenience, smaller bandwidths then available are shown in Figure 6, intending to clearly visualize regions with the largest number of lost bits. The setup parameters are summarized in Table 2. LCR on the left side and AFD on the right symbolically give information on channel quality across specified bands. Higher LCR peaks point on band parts where signal is more fluctuating; therefore, in our transmission scheme selection, we can choose not to use these PRBs, or use them in another scheme (e.g., lower throughput). Regions with longer and shorter duration of fading intervals bring some conclusions about packet loss--Figure 6b for case A, Figure 6d for case B, and Figure 6f for case C.  
Figure 6: Evaluation results for second-order statistics in high-speed train (HST) scenario: (**a**) Case A passband LCR; (**b**) Case A passband AFD; (**c**) Case B passband LCR; (**d**) Case B passband AFD; (**e**) Case C passband LCR; (**f**) Case C passband AFD.  
Using the provided color map, we can perceive that the largest number of lost bits in this scenario is expected in yellow regions (where over 30 wavelengths reside bellow some level), 3500-3500.25 MHz (250 KHz), next about 250/15\(\sim\)16 carriers and 3502.75-3503.5 MHz (750 KHz), or about 750/15\(\sim\)48 carriers; in all, around 64 carriers. Numerically, evaluation results become available for adaptive PRB aggregation (interleaving) to achieve better transmission. The next trial was with second numerology, case B. From Figure 6b, we see more of the spectrum and new part with high AFD, from about 3505-3506 MHz (1 MHz width). Here, we use 20 KHz frequency resolution, as it was enough with respect to carrier distance (30 KHz). In the third trial, for case C, observation band was expanded to 14.4 MHz (Figure 6c). We doubled scanning resolution, as in this case, subcarrier spacing is 60 KHz. It is obvious about the new part shown with long fades and part from 3511 to 3512 MHz (1 MHz) that is considerably free of fades and can be used for higher data rates.
## 4 Discussion  
The duration distributions of the four states shown in Figure 9b indicate that the Poisson distribution was similar to the Gaussian distribution with a larger variance. After experiments, we found that doubling the variance of the Gaussian distribution in the original algorithm could also correct 66/79 arrhythmia errors, which was inconsistent with the conclusion given by [PERSON] et al. (2018). Therefore, due to the development of deep learning and the increased accuracy of classification, the HSS task should involve reducing the influence of the duration probability and optimizing the duration probability model. However, it should be noted that the original Viterbi algorithm (Gaussian, \(\alpha=1\)) can sometimes perform better based on normal heart sounds with severe noise. Thus, continuously improving the algorithms has limitations, so it is equally necessary to reduce the interference due to noise by improving the quality of heart sound collection to support the development of electronic auscultation technology.  
As shown in Figure 14, irrespective of the algorithms or databases, the differences between the direct classification results obtained with training sets and testing sets were large, generally because it is considered that the networks are affected by overfitting problems. The occurrence of overfitting can be addressed by using improved network training techniques, such as drop-out and batch normalization. We used some techniques in our experiments, but the effects were small. Therefore, we considered that the possible main reason for overfitting was the lack of available data for training, thereby making the data distributions in the training sets and testing sets inconsistent, especially in those with diseases. Thus, it is necessary to collect more heart sound recordings from many situations for training in order to improve the performance of the feature classification algorithms.  
Third heart sound (S3) and fourth heart sound (S4) may appear in diastole due to disease ([PERSON] et al., 2017). In terms of the detection of S3 and S4 using the proposed algorithm, the HSMM cannot simply add these two states into hidden states because S3 and S4 are not states of certainty. In order to solve this problem, the algorithm could first assign the signal to S1-systole-S2-diastole, and then perform pattern recognition for S3 and S4 in diastole to detect whether S3 and S4 appear and find their locations. The same method can be used for the diagnosis of heart sound diseases. For example, after performing HSS, the presence of a murmur in systole or diastole can confirm valve problems ([PERSON] et al., 2018), and whether S2 has a heart sound split indicates pulmonary hypertension ([PERSON] et al., 2018). Therefore, detailed annotations are crucial for improving the performance and interpretability of heart sound algorithms. The objectification of heart sound is helpful for doctors to make better diagnoses, thus more doctors can diagnose the patient's heart sound, which in turn will promote the development of heart sound algorithm.
## 4 Analysis of Mechanism of Reduced Blood Pressure and Increased Injection  
### Reduced Solid-Liquid Adhesion  
The adhesion of solid to liquid reflects the retention ability of the solid surface to liquid. The work of adhesion is the maximum work performed outwards by the system during solid-liquid adhesion. Greater adhesion performance corresponded with greater system stability and a stronger solid-liquid interface. Therefore, reducing the adhesion work of the solid-liquid interface can reduce the resistance of liquid flowing through solid surface. In 1952, [PERSON] [31] discussed the interface problem from the view of adhesion work. From the perspective of thermodynamics, he introduced the following solid-liquid adhesion work equation:  
\[W_{a}=\gamma_{gl}+\gamma_{gs}-\gamma_{sl} \tag{4}\]  
Combined with the Young equation, it is as follows:  
\[\gamma_{gs}=\gamma_{gl}\text{cos}\theta+\gamma_{sl} \tag{5}\]  
It follows from Equations (4) and (5) that  
\[W_{a}=\gamma_{gl}(1+\text{cos}\theta) \tag{6}\]  
where \(W_{a}\) is the solid-liquid work of adhesion, mJ/m\({}^{2}\); cos\(\theta\) is the contact angle of a liquid on a solid surface, \({}^{\circ}\); \(\gamma_{gl}\) is the gas-liquid interfacial tension, mN/m; \(\gamma_{gs}\) is the gas-solid interfacial tension, mN/m, and \(\gamma_{sl}\) is the solid-liquid interfacial tension, mN/m.  
According to Equation (6), the adhesion work between a liquid and solid can be obtained only by measuring the surface tension of liquid and the contact angle of liquid on the solid surface, as shown in Table 3. According to the results of wettability evaluation in Section 3.2.1, the solid-liquid adhesion work can be calculated.  
Figure 11: Effect of modified nano-SiO\({}_{2}\) on water injection pressure.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Surface Tension** & **Contact Angle (\({}^{\circ}\))** & **Work of Adhesion** \\  & **(mN\(\cdot\)m\({}^{2}\))** & **Contact Angle (\({}^{\circ}\))** & **(mJ\(\cdot\)m\({}^{2}\))** \\ \hline before the adsorption & & 37.34 & 129.49 \\ after the adsorption & & 134.63 & 21.46 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Effect of modified nano-SiO\({}_{2}\) on solid–liquid adhesion.  
The modified nano-SiO\({}_{2}\) induced a change in the rock surface from hydrophilic to hydrophobic. The contact angle increased, and the adhesion work of the rock to the water phase decreased from 129.49 mJ/m\({}^{2}\) to 21.46 mJ/m\({}^{2}\). These phenomena reduced the ability of the rock surface to bind to the water film and increased its fluidity and effective flow space. Thus, the injection capacity of the water phase improved and the injection pressure was reduced.
# _meta.shrinkage_: An R Package for Meta-Analyses for Simultaneously Estimating Individual Means
## 1 Introduction  
Meta-analysis is a tool for synthesizing statistical results obtained from individually published studies [1]. Meta-analyses have been employed in a variety of scientific studies [2, 3, 4, 5], including studies on the influence of COVID-19 [6, 7, 8].  
Usually, the goal of meta-analyses is to summarize individual studies to find some _common effect_[9, 5]. The idea of estimating the _common mean_ was originated from mathematical statistics and stratified sampling designs under fixed effect models (pp. 55-103 of [10, 11, 12, 13]). In biostatistics methodologies, the estimation method based on random effect models [14] is popular. In either model, the goal of meta-analyses is usually to estimate the common mean by combining the estimators of individual means (Section 2.1).  
In some scenarios, however, the goal of estimating the common mean is questionable. In these scenarios, meta-analyses can still be informative by looking at individual studies' means (e.g., by a forest plot). Aside from these simple meta-analyses, Bayesian posterior means provide a more sophisticated summary of individual means in a meta-analysis [15, 16, 17, 18, 19].  
Recently, [PERSON] et al. [20] proposed non-Bayesian estimators of individual means by applying three methods: the James-Stein (JS) shrinkage estimator, isotonic regression estimator, and pretest (PT) estimator. Their frequentist estimators were shown to be superior to the individual studies' estimators via decision theoretic criteria and Monte Carlo simulation experiments. These frequentist estimators also found their suitable applications to real data examples.  
In this article, we propose a new R package _meta.shrinkage_ in order to implement the frequentist estimators in [20]. Our package can calculate seven estimators (namely \(\delta^{\text{JS}}\), \(\delta^{\text{JS+}}\), \(\delta^{\text{RML}}\), \(\delta^{\text{RJS}}\), \(\delta^{\text{RJS+}}\), \(\delta^{\text{PT}}\) and \(\delta^{\text{GFT}}\); see Section 3 for their definitions). We introduce this R package along with the usage of the R functions and the \"average-min-max\" steps forthe pool-adjacent violators algorithm. We conduct Monte Carlo simulations to validate the proposed R package to ensure that the package can work properly in a variety of scenarios. We made the package freely available in the Comprehensive R Archive Network (CRAN): Available online: [[https://CRAN.R-project.org/package=meta.shrinkage](https://CRAN.R-project.org/package=meta.shrinkage)]([https://CRAN.R-project.org/package=meta.shrinkage](https://CRAN.R-project.org/package=meta.shrinkage)) (accessed on 14 November 2021).  
This article is organized as follows. Section 2 gives the background, including a quick review of meta-analyses. Section 3 introduces the proposed R package. Section 4 conducts simulation studies to validate the proposed R package. Section 5 includes a data example to illustrate the proposed R package. Section 6 concludes the article with discussions. The appendices give the R code to reproduce the numerical results of this article.
## 4 Methodology
### Multi-Step Traffic Prediction  
In Figure 2, the decoder module is employed for multi-step traffic prediction. It utilizes the hidden states from the encoder and the adaptive graph adjacency matrix obtained from the dynamic graph structure learning module to recursively generate multi-step predictions, i.e., the future traffic state. \(\mathcal{L}_{1}\) loss is selected as the loss function:  
\[\mathcal{L}_{1}(\Theta)=\frac{1}{Q}\frac{1}{N}\sum_{t=1}^{t=Q}\sum_{i=1}^{i=N} \left|\hat{X}_{i,t}(\Theta)-X_{i,t}\right| \tag{11}\]  
Here, \(\Theta\) denotes all trainable parameters in the model, \(Q\) is the count of prediction steps, and \(N\) is the quantity of road nodes. \(\hat{X}_{i,t}(\Theta)\) and \(X_{i,t}\) represent the prediction and ground truth of node \(i\) at time \(t\).
# Structured Data REST Protocol for End to End Data Mashup
## 3 Motivational Scenario  
When users are given facility of data mashup at their defined digital place, they will definitely avoid visits of multiple webs/apps and thus network traffic would be reduced up to the certain level. Data mashup fetches the required data from multiple data sources, does the mashup and presents the integrated view to its users. For example, in a public data mashup, a student would like to see all the admission notices of his interested universities/institutes in his personal mashed up page. In case of private data mashup, he would like to see the transaction details of all his bank accounts, review status of his research papers submitted to various journals and results of his all interviews of various recruiters together in a single screen using Single Sign-on (SSO) policy. In such cases, instead of information like various codes/scripts, advertisement, navigation links, menus, images etc., only useful data would be flown in the network.  
Above discussion for performing the public and private data mashup together and presenting their views in a single screen, related to particular user is the main motivation behind this work. The goals of this work are summarized as follows:  
* To provide the public and private both kind of information related to particular user which are scattered throughout the internet, to his/her defined digital location (web/desktop or mobile)
* To implement the single sign-on (SSO) scheme for private data mashup
* To enhance the network performance by allowing the flow of needed data only
* To develop the data mashup technique having user friendly approach for end to end data mashup using standard REST protocol for mashup communication.
# Analysis of the Scalability of UWB Indoor Localization Solutions for High User Densities
## 4 Localization Schemes and MAC Considerations
### Time Difference of Arrival
#### Disadvantages  
* Anchors must be synchronized accurately, which causes synchronization beacons overhead.
* The calculated positions are unknown to the tag, so the estimated position must be communicated via a second network.  
Figure 1: Time Difference of Arrival Scheme.  
Since the mobile tags are only transmitting and not receiving, data aggregation or collaborative localization is not possible.
# A Physical-Layer Watermarking Scheme Based on 5G NR
## 3 System Performance Simulation
### System Performance in the Fading Channel
#### 3.2.1 System Performance under Ideal Carrier Synchronization  
In Figure 17, the symbol error rates of user's signal in Scheme II and Scheme III are consistent, and both are similar to the theoretical symbol error rate of 16 QAM and lower than that in Scheme I. Compared with the theoretical symbol error rate of 16 QAM, there is still a gap between the user's signal error rates in Scheme II and Scheme III, because channel estimation of the DMRS is not completely accurate.  
Figure 16: Success rates of watermark-bit transmission and watermark authentication with a frequency offset in the Gaussian channel.  
Figure 17: Symbol error rate of the user data under ideal synchronization in the fading channel.  
A comparison of the success rate of user-data transmission and watermark authentication in three schemes is shown in Figure 18. The success rate of watermark authentication in Scheme I is far lower than that of user-data transmission, but they are very similar in Scheme II and Scheme III, which proves the importance of channel coding. The success rate of user-data transmission in Scheme II is the same as that in Scheme III, but the authentication success rate in Scheme III is slightly higher than that in Scheme II, which is because pseudo-user-bits are used to generate watermarks in Scheme III.  
Finally, the success rate of watermark-bit transmission and the success rate of watermark authentication are compared in Figure 19. The transmission success rate in Scheme I is much lower than those in Scheme II and Scheme III, and the authentication success rate in Scheme I is limited by the transmission success rate, because Scheme I does not carry out channel coding on the watermark bits. Scheme II and Scheme III have the same success rate of watermark transmission, but the authentication success rate in Scheme III is slightly higher than that in Scheme II, because Scheme III uses pseudo-user-bits instead of user bits as parameters to generate watermarks. The authentication success rate in Scheme II and Scheme III is limited by the success rate of user-data transmission.
# Phase-Matching Quantum Key Distribution with Discrete Phase Randomization
###### Abstract  
The twin-field quantum key distribution (TF-QKD) protocol and its variations have been proposed to overcome the linear Pirandola-Laurenza-Ottaviani-Banchi (PLOB) bound. One variation called phase-matching QKD (PM-QKD) protocol employs discrete phase randomization and the phase post-compensation technique to improve the key rate quadratically. However, the discrete phase randomization opens a loophole to threaten the actual security. In this paper, we first introduce the unambiguous state discrimination (USD) measurement and the photon-number-splitting (PNS) attack against PM-QKD with imperfect phase randomization. Then, we prove the rigorous security of decoy state PM-QKD with discrete phase randomization. Simulation results show that, considering the intrinsic bit error rate and sifting factor, there is an optimal discrete phase randomization value to guarantee security and performance. Furthermore, as the number of discrete phase randomization increases, the key rate of adopting vacuum and one decoy state approaches infinite decoy states, the key rate between discrete phase randomization and continuous phase randomization is almost the same.  
twin-field quantum key distribution; phase-matching; discrete phase randomization; intrinsic bit error rate +
Footnote †: journal:  
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