esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions and Future Work  
Minimum EC is one of the prime requirements in designing routing protocols in UWSNs because of limited resources. The random distribution of nodes, void holes and a decrease in packet drop ratio decreases the lifespan of the network. In this paper, two routing protocols: SPF-Three and BFS-SPF-Three are proposed to attain the reliability in data transmission and energy efficiency using  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Protocols**} & \multicolumn{2}{c}{**Parameters**} \\ \cline{2-3}  & **Achievements** & **Compromised Parameters** \\ \hline FLMPC-One & Less delay & High EC, active nodes and PRR \\ FLMPC-Two & Less reliability and delay & High EC, active nodes and PRR \\ SPF-Three & High reliability, less EC, less active nodes, and affordable PRR & Delay \\ BFS-SPF-Three & High reliability, less EC, high active nodes, and affordable PRR & Delay \\ \hline \hline \end{tabular}
\end{table}
Table 10: Performance Trade-offs between Existing and Proposed Protocols.  
Figure 15: Feasible Region for Noises in UWSNs.  
'Dijkstra' algorithm (a greedy approach for SPF). In both proposed protocols, the binary tree generation starts from a noisy layer to minimize the effect of the existing noises in data packets to get attenuated. This binary tree generation increases data reliability with minimum EC.  
The proposed protocols use proactive approach for opportunistic routing to achieve the energy efficiency, RPER and reduce the number of active nodes to prolong the network lifespan. The simulation results show the efficiency of the proposed protocols in terms of minimum EC. The number of active nodes and RPER are also minimized.  
In future, we will explore and implement these routing protocols with some machine learning and artificial intelligent techniques to avoid void holes. To implement these techniques on test bed for getting more precision in results, will be the new direction of our research. Idea about implementation of \"Internet of things\" is also under consideration.  
[PERSON], K.A.B. and N.J. proposed and implemented the novel schemes; N.G., F.I., M.K.A., M.k.M. and M.A.M completed the mathematical modeling. All authors together refined the manuscript.  
The present research has been conducted by the Research Grant of Kwangwoon University in 2019.  
The authors declare no conflict of interest.
## 2 Related Work
\begin{table}
\begin{tabular}{c c c c c} \hline
**Ref.** & **Theme** & **IIoT** & **IoV** & **IoD** & **Limitations** \\ \hline
[19] & Basic concepts of DT & No & No & No & Only considered DT \\
[20] & DT for networks & No & No & No & Only focused on DT for networks \\
[21] & DT for the business innovation perspectives & Yes & No & No & Only limited to the DT for innovative \\
[22] & DT for IoT & Yes & No & No & Business \\
[23] & DT for sports and ecosystem & No & No & Did not address FL, IoV, or IoD \\
[24] & DT for industrial autonomous systems & Yes & No & No & Only addressed the DT for sports and outdoor activities \\
[25] & Implementation of DT in IoT & Yes & No & No & Limited to the use of DT for the validation \\
[26] & DT in automotive industry & Yes & No & No & of industrial autonomous systems \\
[27] & DT for Industry 4.0 and CPS & Yes & No & No & Addressed FL, IoV, and IoD \\
[29] & DT for NGNs and CPS & Yes & No & No & Only focused on the application of DT in the automotive industry \\
[30] & FL for the blockchain enabled IoV & No & Yes & No & Included FL, IoV, and IoD \\
[31] & DT for the deployment of IoD & No & No & & Scope of survey did not include FL, IoV, or IoD \\
[32] & Basic concepts and challenges of FL & No & No & No & Similarly, IoV and IoD were not included in scope \\
[33] & FL for enabling technologies & Yes & No & No & Only considered the FL for the IoV based on the blockchain \\
[34] & FL applications, privacy, security, and challenges & No & No & Yes & Focused on the ML algorithms and DT for IoD \\
[35] & Security and privacy of FL & No & No & No & Only limited to the definitions, applications, and challenges of FL \\
[36] & Data privacy and protection using FL & Yes & No & No & Focused on the emerging technologies, applications, and protocols of FL \\
[37] & FL in preserving data & Yes & No & No & Addressed DT, IIoT, IoV, and IoD \\ \end{tabular}
\end{table} 
Table 2: Summary of the existing literature works on DT and FL. By analyzing Table 2, it is evident that no survey has simultaneously discussed the use of DT and FL for IIoT, IoV, and IoD. Thus, a survey is needed that discusses DT and FL for the IIoT, IoV, and IoD.
# Design of a Filtering Power Divider with Simple Symmetric Structure Using Stubs
## 2 Design of the Power Divider Circuit
### Typical Power Divider  
Figure 1 shows a typical WPD including two main quarter-wavelength branches and a single resistor, where the resistor is used to provide isolation in the device. The main branches' impedance is 70.7 \(\Omega\) and their electrical length is 90\({}^{\circ}\).  
A typical WPD is simulated on the Rogers/5880 substrate with ADS momentum software. The thickness, loss tangent, and \(\varepsilon_{\mathrm{r}}\) of the applied substrate are 0.508 mm, 0.0009, and 2.2, respectively. The frequency response and layout of the typical WPD at an operating frequency of 2 GHz are depicted in Figure 2.  
It can be concluded from the typical WPD results that the WPD does not have a compact size and it does not have the desirable suppression band, which may result in the presence of unwanted harmonics. In the following sections, it will be explained that the proposed divider can improve the scattering parameters and also achieve a desirable suppression band and compact size.
# Empirical Evidences for Urban Influences on Public Health in Hamburg
## 3 Results: Identifying Urban Influences on Health
### Answering the Research Question  
The results of the LR address the research question of which factors have the greatest influences on UrbWelth: the determined variables can be seen in Figure 4 with additional information given on the distributions of answers. Taken together, urban variables account for 12% of the variance of EQVAS (SRH), whereas the confounding variables account for 15%. Thus, they contribute to the main influential (confounding) factors, but they are subject to a different approach in terms of urban governance measures (see Section 4).  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Model** & **Variable** & **Estimation** & **Std. Error** & **\(t\)-Stat** & **\(p\)-Value** \\ \hline \multirow{4}{*}{1} & SMO & 1.162 & 0.446 & 2.604 & 0.009 b  \\  & AGE & -0.237 & 0.028 & -8.505 & 0.000 b  \\  & INC & 1.226 & 0.184 & 6.682 & 0.000 b  \\ \hline \multirow{4}{*}{2} & SMO & 1.055 & 0.417 & 2.531 & 0.012 c  \\  & AGE & -0.241 & 0.026 & -9.108 & 0.000 b  \\  & INC & 0.831 & 0.175 & 4.744 & 0.000 b  \\  & QCA & -0.726 & 0.365 & -1.989 & 0.047 c  \\  & SOC & -1.379 & 0.421 & -3.276 & 0.001 b  \\  & NHI & -2.488 & 0.479 & -5.194 & 0.000 b  \\  & CNI & 2.389 & 0.731 & 3.269 & 0.001 b  \\  & HDO & 3.641 & 0.568 & 6.409 & 0.000 b  \\ \hline \end{tabular}  
* \({}^{\text{a}}\) Dependent Variable: EQVAS, b \(p\) < 0.001, c \(p\) < 0.05.  
\end{table}
Table 3: Coefficients a and \(p\)-values for linear regression (LR) models.  
To answer the question of multi-influences or -stressors, the urban variables can be used to identify hotspots of aggregated burden (Figure 5). Taking into account the areas that deviate by more than one SD (from the respective mean values of the identified urban variables) shows that the burdens of urban life are distributed quite unevenly over the study areas. Three areas with a four-fold or triple burden can be identified: they are all located in the Wilhelmsburg and Hausbruch districts of Hamburg. These neighborhoods are also characterized by low socioeconomic status, making it even more appropriate to speak of a four-fold or five-fold burden.  
Figure 4: Relevant urban variables (rating of neighborhood health infrastructure (NHI), rating of one’s social network (SOC), rating of neighborhood air quality (QCA), cold stress (night/winter/indoors) (CNI), heat stress (day/summer/outdoors) (HDO); identified by LR) and dependent variable (EQVAS).  
Figure 5: Location of study areas and burden by the identified urban variables (>1 SD from mean).
# Incorporating Code Structure and Quality in Deep Code Search
## 2 Related Work
### Attention Mechanism  
When embedding sequential inputs into a vector space, RNN assigns the same information weight to each input feature. However, since different features of code snippets have different qualities, it is necessary to give higher weight to the high-quality features. The problem of assigning different weight to different features has been extensively studied, and one of the most effective methods is the attention mechanism [14; 22; 23; 24; 25].  
Attention method contains a randomly initialized global attention vector \(\alpha\in R^{d}\). For a set of combined context vectors \(\widetilde{c_{1}},\widetilde{c_{2}},\ldots,\widetilde{c_{n}}\), attention method calculates an attention weight \(\alpha_{i}\) for each \(\widetilde{c_{i}}\) as the normalized inner product between the context vectors and the global attention vector \(\alpha\):  
\[\alpha_{i}=\frac{exp(\widetilde{c_{i}}^{T}\cdot\alpha)}{\sum_{j=1}^{n}exp( \widetilde{c_{j}}^{T}\cdot\alpha)} \tag{8}\]\[v=\sum_{i=1}^{n}\alpha_{i}\cdot\widetilde{c_{i}} \tag{9}\]  
where \(\alpha_{i}\) is a softmax function in conventional form. According to the properties of the softmax function, the attention weights are positive and add up to 1. Attention weight \(\alpha_{i}\) can be regarded as a weighted average and can be trained to represent the importance of combined context vectors. Finally, representative vector \(v\in R^{d}\) combines the characteristics of each context vector. In the domain of code search, [PERSON] et al. have introduced the attention mechanism to weight the critical paths of codes [14]. In our work, we leverage attention mechanism to solve the problem of different code quality.
# Ground Based Hyperspectral Imaging to Characterize Canopy-Level Photosynthetic Activities
## 4 Discussion
Future studies, therefore, need to validate the efficacy of the three models used in the present study and examine new ways to estimate maximal fluorescence using passive sensing methods. For instance, maximal fluorescence estimation models for stressed plants can be established and validated in a full PAR range from 0 to 6000 \(\upmu\) mol/m\({}^{2}\)/s in a controlled environment, and transferred to field applications where data are acquired in a part of the full PAR range. We should also acknowledge some other limitations identified in the present study. First, the present study was based on a single-year, single-location field experiment, which might present concerns (e.g., environment variations) from the agronomic viewpoint. The present study, however,focused on methodology development to advance sensing and data analytics that can be potentially used for agricultural applications. Specifically, we aimed to develop and validate a ground hyperspectral imaging-based approach for diurnal SIF measurement and canopy-level photosynthesis characterization. In this context, diurnal data collection and trait extraction can be considered repeated experiments. High correlations between imaging- and fluorometer-derived measurements suggest high accuracy and repeatability of the developed system and analysis method, which, together, meet the study goal and objectives. Nonetheless, multi-year, multi-location experiments will be required to reveal agronomic and physiological findings in the future. Second, we only validated the developed approach through measurements (i.e., effective quantum yield of PSII and ETR) that can be obtained from a PAM-based fluorometer, which might limit the use of the approach for studies broadly related to photosynthesis. It is necessary to conduct successive experiments to comprehensively validate the developed approach through measurements that can be obtained by other well-established methods, such as SIF from instruments with high spectral resolution and gas exchange measurements.
# Interdisciplinary Urban Tunnel Control within Smart Cities
## 3 New Road Tunnel Traffic Control Algorithm
### Case Study Results--Control of Road Urban Tunnel Blanka in Prague  
In this section, we demonstrate the effect of closing the tunnel Blanka using a BEFORE and AFTER analysis. Figure 14 shows the development of the speed index for one working day and one weekday before, resp. after the tunnel opening. The figure demonstrates that the speed index dropped more substantially before opening of the tunnel Blanka.  
The average speed index increased from 0.909 to 0.915 for a weekend day before, respectively, after and from 0.857 to 0.865 for a selected Tuesday before, respectively, and after. The actual economic impact is demonstrated in Table 1. It uses the presented cost of congestion computations. It provides aggregated values for all traffic sensors presented in Figure 2 and always the whole day. Please note that we are looking at the impact on the rest of the city and not values in the tunnel.  
The table presents the following variables:  
* Sum of flow (veh/day), which takes the daily total number of vehicles on each sensor (without the traffic flow in the tunnel) and sums these numbers;
* Total cost, which provides the aggregated and cumulative cost of congestions in millions of Czech crowns;  
Figure 14: The average speed index over all sensors during the measurement days.  
* Index, which is just an indication of the impact of the daily sum of flows on the total price by dividing it by the variable sum of flows;
* Average speed.  
The first part of the table focuses on weekends. We can see that there was a decrease in the total flow outside of the tunnel (\(-2.21\%\)). This can mean that since the traffic is lower, more people selected to travel through the tunnel. This leads to a decrease in the total price for congestion by about 623 thousand CZK per day.  
During the week, there is not only the traffic in the tunnel, but the sum of flows within the city increased by over 2.2%. Even for this increase in traffic flow, there is a decrease in the cost of congestion of about 596 thousand CZK. In both cases we also observe an increase in the average speed and especially for the weekday decrease in the Index.  
Even more substantial increases of traffic (and thus deterioration of the traffic flow characteristics such as average speed) is expected if the tunnel is closed nowadays. All of the induced traffic will play an essential role to that. The tunnel attracts daily over 80,000 vehicles that in case of closure of the tunnel must disperse into the city.
# A Lightweight High Definition Mapping Method Based on Multi-Source Data Fusion Perception
## 4 Fusion Mapping on Cloud Server
### Global Pose Graph Optimization  
In order to build a globally consistent semantic map, it is necessary to unify the local trajectory and semantic elements of LSMs for different vehicles and time periods with the global coordinate system (ENU coordinate system) since they are relative to the vehicle body coordinate system at a certain time. This is achieved by aligning the odometry pose of the LSMs with the global coordinate system. To accomplish this, we construct global pose graph optimization by fusing the LSM-associated local trajectory and GPS trajectory, as shown in Figure 6a. The optimization problem can be expressed as:  
\[\mathbf{\chi}^{*}=\mathbf{argmax}_{\mathbf{\chi}}\sum_{t=0}^{n}\left\|\mathbf{e}^{l}(\mathbf{z}_{ t-1,\mathbf{t}^{\prime}}^{\mathbf{l}},\mathbf{\chi})\right\|_{\mathbf{\Omega}_{t}^{\mathbf{\chi}}}^{2}+ \left\|\mathbf{e}^{\mathbf{\varepsilon}}(\mathbf{z}_{t}^{\mathbf{\varepsilon}},\mathbf{\chi}) \right\|_{\mathbf{\Omega}_{t}^{\mathbf{\varepsilon}}}^{2} \tag{4}\]  
Figure 4: Example of structural semantic element map.  
\[e^{l}(z_{t-1,t}^{l},\chi)=\begin{bmatrix}q_{t-1}^{l}-1\\ q_{t-1}^{l}-1\\ \end{bmatrix}\odot\begin{bmatrix}q_{t-1}^{w}-1\\ q_{t-1}^{w}-1\\ \end{bmatrix} \tag{5}\] \[e^{g}(z_{t}^{g},\chi)=p_{t}^{\text{GPS}}-p_{t}^{w} \tag{6}\]  
where \(\Omega_{t}^{l}\) and \(\Omega_{t}^{g}\) denote the covariance of odometer error and GPS measurement error, respectively, \((p_{t-1}^{l},q_{t-1}^{l})\) represents the local pose at \(t-1\), and \(e^{l}\) and \(e^{g}\) represent the relative pose constraint and the global absolute position constraint, respectively. The optimization goal is \(\chi=\{x_{0},x_{1},\ldots,x_{n}\}\), where \(x_{i}=\{p_{i}^{w},q_{i}^{w}\}\).
# Creating Audio Object-Focused Acoustic Environments for Room-Scale Virtual Reality
## 6 Discussion
### Conversion of Ambionic Field Recordings  
Ambionic field recordings used as acoustic backdrops in outdoor environments may require conversion to an object-based representation to comply with the object-focused approach and to facilitate acoustic coherence. For example, the combination of bird-like sounds from different Ambisonic and non-Ambisonic field recordings found in a sound library may cause narrative/aural contradictions. Birds from one recording may not be compatible with the environment suggested by another recording through a mismatch of bird species or reverberation cues, e.g., dense vs. sparse forest. Such a mismatch could be avoided if the sounds captured in an Ambisonic field recording could be extracted and thus converted into an object-based representation.  
Such conversion may be possible in cases where the recording features spectral or temporal separation between the foreground and background sounds and the sounds of interest are in the foreground. For example, insects in an Ambisonic recording could be isolated from atmospheric background sounds, e.g., very distant traffic hum, via bandpass or FFT filters and then decoded to mono sound files. Each insect call could be separated into a sound file, spatialized using object-based rendering, and the frequency of the insects' utterances recreated using a rule-based system. More advanced machine learning approaches for sound isolation could be used alternatively [72]. The remainder of the Ambisonic recording could be recycled as a diffuse ambient Ambisonic bed if all identifiable gestural elements have been removed. The authors used this process in all outdoor environments in _Planet Xerilia_ (Video S7: 07_solution.mp4).
# Probabilistic Condition Monitoring of Azimuth Thrusters Based on Acceleration Measurements
## 2 Materials and Methods
### Condition Monitoring Algorithm
#### 2.2.6 Multivariate Normal Distribution  
The model identification is done based on training data consisting of machine operation from periods where an undamaged (or healthy) condition is probable. It is assumed that the residuals follow the identified normal distributions when the system is in undamaged condition. Therefore, the residuals are modeled as multivariate normal vectors \(x\) that belong to a multivariate normal distribution with mean vector \(\mu\) and covariance matrix \(\Sigma\). The probability density function (PDF) of the multivariate normal distribution function is  
\[f(x,\mu,\Sigma)=\frac{1}{\sqrt{\left|\Sigma\right|(2\pi)^{d}}}exp\left(-\frac {1}{2}(x-\mu)\Sigma^{-1}(x-\mu)^{T}\right), \tag{6}\]where \(d\) is the dimension of the multivariate normal distribution. In this study, the covariance matrix for each distribution was defined based on normalized residuals (\(\mu=0\), \(\sigma=1\)).  
For each multivariate normal distribution, the identification of several parameters is required. The parameters are summarized in Table 5 and correspond to the \"model parameters\" identified from data, as shown in Figure 2. Different parameter values are identified for different shaft speed areas.  
In this study, separate models were used for time domain features (no. 1-3), bearing features (no. 5-8) and gear features (no. 9-11), because the algorithm should have diagnostic ability. Therefore, three covariance matrices were identified for each shaft speed range. In total, 47 parameters were identified and saved for each selected shaft speed range with these settings. The means of residuals (\(\mu\)) are approximately zero and can be replaced with zeros.
# Susceptibility Modeling of a Rubber Track for Lightweight Mobile Robots
## 5 Model Validation  
In order to determine the parameters of angular stiffness \((k_{z})\) and angular damping (\(c_{z}\)), computer simulations were carried out. The track belt was formed into a circle and lowered onto a flat surface. After the track belt was formed by its own weight, the static test parameters were determined. Furthermore, the tracks were loaded with 1.3 kg of weight, and the oscillation parameters were determined. Comparing simulation parameters from static and dynamic tests with the experimental results, stiffness and damping in track belt constraints were set up (Figure 6).  
In order to match the simulation results with the experimental tests as precisely as possible, three methods of angular stiffness definition in the constraints (Figure 6a) were compared:  
* Method I: constant stiffness value \(k_{z}\) regardless of the angle \(\zeta_{Z1}\) (Figure 6b);
* Method II: bivalent stiffness--two different values \(k_{z1}\), \(k_{z2}\) for bending track belt inwards and outwards (Figure 6c);
* Method III: variable stiffness value in the whole range of angles \(\zeta_{Z1}\). In this method, instead of the stiffness coefficient \(k_{z}\), the torque--rotation angle characteristic is used in constraints as a nonlinear function \(M_{z}=f(\zeta_{Z1})\) (Figure 6d).  
The constraint modeling methods were compared with each other on the basis of their relative errors. In addition, it was established whether the modeling method would affect the simulation-determined track belt bending resistance. For each method, the drive torque needed to scroll the track at a constant speed between a drive wheel and an idler without contact with the ground was determined.  
Figure 5: The three exemplary track oscillation plots, recorded in dynamic test of track 1.
# Myths and Realities about Genetically Modified Food: A Risk-Benefit Analysis
## 3 Myths and Realities of GM Crops: Risk Assessment
### Allergenicity Studies  
In the USA, concerns have been voiced about the possibility of allergic reactions as a result of consuming GM foods. Consequently, several GM crops are unapproved for human consumption by the US authorities [8]. For example, a 2S albumin gene from Brazil nut has been inserted into a soybean cultivar for nutritional purposes. However, the GM product has been demonstrated to pose a risk of human allergic reactions, particularly in people with Brazil nut allergy [8, 73]. Concerns about the Cry9C protein, a type of insect pest-resistance protein, have been voiced, given its greater heat stability and a possible longer digesting time. Yet, no direct association exists between allergic reactions and consuming GM food [8, 74].  
The WHO declared that it is not possible to generalize about the safety of GM crops, and, instead, each case must be individually assessed [72]. Marketed GM crops have previously passed risk assessments, including allergenicity tests; no case reports exist about the immunotoxic effects or allergic reactions that result from consuming them. In humans, no technique has been established to predict allergic responses to non-endogenous proteins. According to the EFSA, animal models should be employed to evaluate the sensitizing potential of new proteins on a case-by-case basis [75]. Rodent models are more frequently utilized for immunotoxicity and allergenicity studies [76]. Whether these models accurately describe the allergenicity of proteins in humans or cattle is a matter of debate. Thus, developing validated standardized animal models to assess allergenicity is vital [8]. Apartfrom animal models, in vitro digestibility tests, amino acid sequence homology, and serum screening are currently the most popular methods to determine allergenicity [77] (Table 2).