esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Surrogate Model Establishment
### Surrogate Modeling Based on Generalized Regression Neural Network  
The GRNN, a probabilistic neural network variant, enables rapid prediction without iterative training due to its fixed architecture governed by a single hyperparameter \(\sigma\) (spread factor) [4]. Unlike backpropagation-based methods, GRNN eliminates weight optimization, making it computationally efficient for small-to-medium datasets [36]. As  
Figure 9: Sampling results of the optimized Latin hypercube design: (**a**) 3D spatial distribution of experimental points; (**b**) projection of experimental points on Dimension 1 and Dimension 2; (**c**) projection of experimental points on Dimension 1 and Dimension 3; and (**d**) projection of experimental points on Dimension 2 and Dimension 3.  
illustrated in Figure 10, the GRNN architecture consists of four distinct processing layers: input layer, pattern layer, summation layer, and output layer.  
The input layer dimension corresponds to the feature space of training samples. In the pattern layer, each neuron computes the Gaussian-transformed Euclidean distance between the input vector \(X\) and the \(i\)-th training sample \(X_{i}\):  
\[D_{i}=\exp\biggl{(}-\frac{\|X-X_{i}\|^{2}}{2\sigma^{2}}\biggr{)} \tag{4}\]  
where \(\|\cdot\|\) denotes the Euclidean norm.  
The summation layer integrates contributions from all pattern neurons through two operations:  
1. \(S\)-summation: Weighted sum of target values: \[S=\sum_{i=1}^{n}Y_{i}D_{i}\] (5)
2. \(D\)-summation: Total activation sum: \[D=\sum_{i=1}^{n}D_{i}\] (6)  
The network output \(Y(X)\) is derived as a normalized weight average:  
\[Y(X)=\frac{S}{D}=\frac{\sum_{i=1}^{n}Y_{i}\exp\Bigl{(}-\frac{\|X-X_{i}\|^{2}}{2 \sigma^{2}}\Bigr{)}}{\sum_{i=1}^{n}\exp\Bigl{(}-\frac{\|X-X_{i}\|^{2}}{2\sigma ^{2}}\Bigr{)}}. \tag{7}\]  
The architecture inherently performs kernel regression, with \(\sigma\) controlling the smoothness of predictions [37]. The optimal spread factor \(\sigma\) was determined through 5-fold cross-validation, with \(\sigma\) = 0.16 identified as achieving the best trade-off between flexibility and generalization performance. This value minimizes validation errors while preventing overfitting to specific training samples.
## 4 Discussion
### Limitations and Prospects  
The carbon, water, and energy flux estimations for the regional scale based on the ML models represent a process of upscaling ecological knowledge of the carbon cycle from the site scale. Therefore, it is essential to evaluate the spatial representativeness of the flux observation sites [86; 87]. Most of the current flux observation sites were established gradually over the previous few decades, with their locations often having been determined by expert experience and consideration of accessibility, including traffic, logistics, and topographic factors. Site density and uniformity substantially vary in different regions, owing to the lack of a systematic design. The 18 flux sites used in this study exhibited a non-uniform spatial distribution (Figure 1). In situ observations representing a few surrounding kilometers cannot fully represent the climate, environment, and flux in remote regions [88]. This increases the level of uncertainty in regional-scale carbon-flux estimations [89; 90]. It has been reported that the current network of flux towers in northwestern Tibet lacks representativeness, and that adding flux towers could considerably improve the ability of the existing flux network to represent local environmental conditions [91]. It is expected that the ecological environment will receive more public attention, and the rapid expansion of big data in ecological systems engineering will establish an increasing number of flux sites, so their representativeness will become more credible.  
The time range of carbon flux data employed in this study was concentrated in the period 2003-2014 (Table 1). This approximately coincides with the long-term regional estimation of RE. However, we obtained only one-year flux data for the AR, NMC, ZF, HLBE, SZWQ, and XLS sites. The small amount of observation data not only means these sites were under-represented, but may also make it impossible for the Geoman model to learn adequately the features of these flux data during model training. The estimated RE in this study is based on time-series environmental data with an eight-day resolution. These environmental variables cannot fully reflect various extreme climatic events that occurred during the eight-day interval [92], such as extreme heat waves or rainfall in summer and extreme cold in winter. The limited availability and considerable uncertainty of SOC and soil texture data restrict their effective application in estimating the RE at the regional scale [17; 59; 93]. The continuous sharing of ecological data, and the development of data augmentation and digital soil mapping technologies will provide reliable solutions to these problems.
# DARAL: A Dynamic and Adaptive Routing Algorithm for Wireless Sensor Networks
## Appendix B DARAL Parameter Appendix  
This appendix describes the different parameters of _DARAL_. A routing algorithm with an elevated number of parameters could be difficult to handle, but also allows a better tuning for system, compared to other fixed algorithms.  
As every network layer algorithm and protocol, DARAL needs different timers regulating its functionality. Those timers regulate the answer of the algorithm against different events. The list of timers for DARAL is:  
* \(T_{link}\): The timer for link evaluation is fired when a node receives the first answer from a VC. \(T_{link}\) represents the time that a certain node awaits to receive more answers from different VCs. Once this timer is expired, the connection process starts.  
* \(T_{alive}\): This timer fires the maintenance task of the network topology. Once it is expired, a certain VC tests its own sub-network, checking the connectivity with every node.
* \(T_{down}\): This timer is used by a VC to protect its sub-network from node drops. When a certain node surpasses \(T_{down}\) without responding messages from its VC, the VC proceeds to remove the node from the sub-network, informing to the topology about this drop.
* \(T_{reconnect}\): This timer is used for two different tasks. First, it is used by a VC without vID, to resend the vID request to its VC. Secondly, it is used by non-connected nodes to restart the network stack, restarting also the connection process (sending a new broadcast asking for a valid link).
* \(T_{ack}\): The timer for acknowledgement (ACK) measures the time that a certain message is valid. If an ACK is not received before the timer expiration, the packet should be resend. This kind of timer is typical in the network layer.  
_DARAL_ also makes use of two different thresholds to delimit the minimum link quality and the role played by a node:  
* \(TH_{baselved}\): The base level threshold represents the minimum LQI that a link between nodes should have. If a link is not above this threshold, the node itself discards the connection and looks for another one.
* \(TH_{role}\): The role threshold is used to determine the role of each node in the network. If LQI is above \(TH_{role}\), a node connects to the network as EN, representing a good QoS and allowing that node to save energy adjusting its radio transceiver and reducing the interference with other nodes. If LQI is below \(TH_{role}\), a node connects itself as VC, representing an extension for the coverage of the network. Finally, \(DARAL\) makes use of another parameter (\(L_{nodes}\)) that balances the load of sub-networks:
* \(L_{nodes}\): The node limit is a parameter that set the maximum number of nodes that a VC can handle. This limit balances the load between different sub-networks, creating areas with restricted traffic, due to the locality principle.  
All of these parameters could be tuned by intelligent systems, opening a vast research line, in order to adapt them to diverse scenarios, not only for Smart Cities, but also for IoT.
# Neural-Network-Assisted Polar Code Decoding Schemes
## 4 Numerical Simulation
### Performance Comparison under AWGN Channel  
This section simulates the BER performance comparison of different algorithms with a code length of \(N=1024\) and a code rate of \(R=0.5\). The BER performance of the LSNNAD  
Figure 14: Decoding results of different code rates.  
Figure 15: Decoding results of different code lengths.  
and that of the KSNNAD are compared with those proposed in [17; 19; 25; 33; 39]. The simplified SC decoder proposed in [17] is marked as SSCD [17], the SCL decoder proposed in [25] with a list length of 2 is marked as SCLD L = 2 [25], the fast SC flip decoder proposed in [19] with \(T_{max}\)= 2 is marked as FSCFD \(T_{max}\)= 2 [19], the recursive trellis decoding scheme proposed in [39] with \(\tau\)= 4 is marked as RTD \(\tau\)= 4 [39], and so on. The results are shown in Figure 16.  
As shown in Figure 16, the BER curves of SCD, SSCD and SCLD with L = 1 are just the same. The BER performance of LSNNAD scheme was slightly worse than NSCD and RTD with \(\tau\)= 6, whereas the performance of KSNNAD is better than FSCFD with \(T_{max}=3\) and worse than SCL decoder with \(L=2\).
# Thermoelectricity Modeling with Cold Dipole Atoms in Aubry Phase of Optical Lattice
## 3 Results
### Density Dependence of Aubry Transition  
The dependence of Aubry transition point \(K_{c}(v)\) can be obtained from the local description of the map (2) by the Chirikov standard map. For that, the equation of \(x_{i+1}\) in (2) is linearized in  
Figure 1: Functions related to the dynamical map obtained from the ground state equilibrium positions \(\{x_{1},\ldots,x_{N}\}\) of the atoms at \(K=0.02\) (**left** column) and \(K=0.1\) (**right** column). Top row: the red circles show the reconstructed kick function \(g(x)\) using the the ground state positions \(\{x_{1},\ldots,x_{N}\}\). The dashed curved gives the theoretical form of the kick function \(g(x)=-\sin x\). Middle row: phase space portrait of the \((x,p)\)-map (2) (green points) and the actual ground state positions of the atoms (red circles). Bottom row: the atom positions are shown via the hull function \(h(x)=(x_{i}+\pi)[\text{mod }2\pi]-\pi\) versus \(x=(2\pi(i-1)/\
u+\pi)[\text{mod }2\pi]-\pi\); the positions of the two atoms at the chain ends are fixed. Here we have \(N=89\) atoms and \(L=55\) periods, \(\
u=89/55\).  
Figure 2: **Left** panel: phonon spectrum \(\omega(k)\) for \(K=0.02\) (open circles) and \(K=0.1\) (full circles) for \(\
u=N/L=89/55\). **Right** panel: dependence of the lowest phonon frequency \(\omega_{0}\) on \(K\) for the lattice with \(N\) dipole atoms in \(L\) lattice periods at density \(\
u\approx\
u_{g}=1.618...\): \(N/L=55/34\) (blue curve), \(89/55\) (green curve), \(144/89\) (black curve); the arrow marks the point of the Aubry transition at \(K_{c}=0.019\) defined as it is described in the text.  
near the resonant value \(p_{r}\) that leads to the standard map form \(y_{i+1}=y_{i}-K_{\rm eff}\sin x_{i}\), \(x_{i+1}=x_{i}-y_{i+1}\) with \(y_{i}\propto p_{i}\) and \(K_{\rm eff}=(2\pi)^{5}K/(12\
u^{5})\) (see details in Appendix A and [9]). The last KAM curve is destroyed at \(K_{\rm eff}\approx 1\)[2; 3] that gives  
\[K_{c}\approx 12(\
u/2\pi)^{5}\approx 0.0136(\
u/\
u_{g})^{5}\,\ \
u_{g}=1.618... \tag{3}\]  
The numerically obtained dependence \(K_{c}(\
u)\) is shown in Figure 3 for three different chain lengths \(L\). On average, it is well described by the theory (3) taking into account that \(K_{c}\) is changed by almost four orders of magnitude for the considered range \(0.5\leq\
u\leq 3.5\). There are certain deviations in the vicinity of integer resonant densities \(\
u=1,2,3\) which should be attributed to the presence of a chaotic separatrix layer at such resonances that reduces significantly the critical \(K_{c}\) for KAM curve destruction (similar effect has been discussed for the ion chains [9]).
## Appendix A  
The top five or six IMs in terms of different matrices are indicated in this demand models and IM comparisons with boldfaces.  
\begin{tabular}{c c c c c c c c c} \hline \hline  & & \(R^{2}\) & \(\beta\) & \(b\) & \(\zeta\) & \(R^{2}\) & \(\beta\) & \(b\) & \(\zeta\) \\ \hline
**IM Type** & **IM** & & **EDP: Max Acceleration** & & & & & **EDP: Max Displacement** & \\ \hline  & PGA & **0.903** & **0.256** & **0.970** & **0.264** & 0.533 & 0.576 & 0.764 & 0.754 \\  & CAV & 0.592 & 0.526 & 0.846 & 0.622 & 0.616 & 0.523 & 0.884 & 0.591 \\  & Ia & 0.773 & 0.392 & 0.495 & 0.793 & 0.634 & 0.510 & 0.459 & 1.111 \\  & Ic & 0.784 & 0.382 & 0.679 & 0.563 & 0.612 & 0.526 & 0.615 & 0.855 \\ _Acceleration-related_ & A\({}_{\text{rms}}\) & 0.764 & 0.400 & **1.021** & 0.392 & 0.535 & 0.575 & 0.877 & 0.656 \\  & SMA & **0.837** & **0.332** & 0.947 & **0.350** & 0.582 & 0.545 & 0.809 & 0.675 \\  & EDA & **0.910** & **0.247** & **0.971** & **0.255** & 0.535 & 0.575 & 0.763 & 0.754 \\  & A95 & **0.905** & **0.254** & **0.967** & **0.262** & 0.531 & 0.578 & 0.759 & 0.761 \\  & ASI & **0.873** & **0.293** & **0.968** & **0.303** & 0.562 & 0.558 & 0.796 & 0.701 \\  & \(S_{a}\) (\(T_{1}\)) & 0.379 & 0.648 & 0.586 & 1.107 & **0.806** & **0.371** & 0.876 & **0.423** \\ \hline  & PGV & 0.696 & 0.454 & 0.942 & 0.482 & **0.741** & **0.429** & **0.996** & **0.431** \\  & V\({}_{\text{rms}}\) & 0.427 & 0.623 & 0.797 & 0.781 & 0.641 & 0.505 & **1.002** & **0.504** \\  & SED & 0.400 & 0.637 & 0.352 & 1.810 & **0.673** & 0.482 & 0.468 & 1.029 \\ _Velocity-related_ & SMV & 0.615 & 0.510 & 0.895 & 0.570 & **0.733** & **0.436** & **1.002** & **0.435** \\  & \(S_{p}\) (\(T_{1}\)) & 0.488 & 0.589 & 0.704 & 0.836 & **0.817** & **0.361** & 0.934 & **0.386** \\  & HI & 0.642 & 0.492 & 0.877 & 0.561 & **0.882** & **0.290** & **1.054** & **0.275** \\  & VSI & 0.728 & 0.429 & 0.952 & 0.451 & **0.863** & **0.312** & **1.062** & **0.294** \\ \hline _Displacement-related_ & PGD & 0.138 & 0.764 & 0.296 & 2.576 & 0.343 & 0.683 & 0.479 & 1.427 \\  & D\({}_{\text{rms}}\) & 0.052 & 0.801 & 0.175 & 4.568 & 0.200 & 0.754 & 0.352 & 2.142 \\  & \(S_{d}\) (\(T_{1}\)) & 0.376 & 0.650 & 0.583 & 1.114 & **0.805** & **0.373** & 0.875 & **0.426** \\ \hline _Time-related_ & V/A & 0.138 & 0.764 & 0.684 & 1.117 & 0.008 & 0.840 & 0.170 & 4.941 \\ \hline \hline \end{tabular}  
The illustration of all IM parameters of a ground motion is provided below:  
\begin{tabular}{c c} \hline \hline
**IM (Unit)** & **Value** \\ \hline PGA (g) & 0.151 \\ PGV (cm/s) & 8.866 \\ PGD (cm) & 3.599 \\ PGV/PGA (s) & 0.059 \\ ARMS (g) & 0.034 \\ VRMS (cm/s) & 2.535 \\ DRMS (cm) & 1.397 \\ Ia (m/s) & 0.388 \\ Ic & 0.029 \\ \hline \hline \end{tabular}
# Vendor-Agnostic Reconfiguration of Kubernetes Clusters in Cloud Federations
## 8 Discussion of Limitations
### Barriers in Edge and Fog Computing Solutions  
Kubernetes was initially designed for cloud-native applications. Due to the specific nature of edge and fog computing solutions, the K8s open-source distribution had to be shrinked to meet energy and resource scarcity constraints of edge devices; moreover, the architecture of the control plane also had to be adapted in order to meet performance, availability and scalability requirements [7]. As such, it may arise that many features of K8s do not make sense anymore in edge and fog computing solutions. For example, the static CPU management policy feature does not make sense on nodes with a single CPU core. Moreover, existing feature configurations and implementations also need to be re-engineered to meet the aforementioned requirements.  
The overall architecture of K8s should therefore be re-engineered using product line engineering methods. For example, the service line engineering method uses feature modeling techniques for the customization of multitenant SaaS applications [58]. It could be used in K8s to model the subset of all possible K8s feature combinations that are valid to use for a particular tenant of a K8s cluster and the subset of valid feature configurations and associated plug-in components that yield from a particular combination of features.
###### Abstract  
In precision farming technology, the interest of the researchers has been focused on the applications of autonomous mobile robots for agricultural operations such as planting, inspection, spraying, and harvesting. However, each autonomous robot generally performs a single agricultural task. In this context, complete autonomy in precision farming can be achieved by using coordinated multi-robot systems that can easily and safely cooperate to accomplish agricultural tasks. The efficiency of the multi-robot system depends on the number of robots, the size of the robots, the distance between each robot, the instant location and heading angle of the robots, and the size of the farmland. This paper describes the development of wireless Robot to Robot (R2R) communication system architecture and the collision avoidance algorithm for multi-robot precision farming applications. The developed system uses the fusion of a digital compass and GPS receiver for wirelessly broadcasting the spatial and temporal data of the mobile robots through WiFi. In this study, WiFi broadcasting was chosen for reasons such as the advantages of long wireless signal range and strength, not being easily affected by weather and dust, low cost, and so on. The proposed system realizes the real-time wireless broadcasting of the mobile robot information for eliminating the collision of mobile robots and improving the level of safety management. The results show that the system has flexible, reliable, and adaptable solution, and thus can increase the efficiency of the multi-robot system in precision farming applications.  
robot-to-robot communication system; multi-robot system; WiFi; collision avoidance; precision farming 2023  
12 June 2023  
12 July 2023  
21 July 2023  
24 July 2023  
1
## 3 Problem Statement and Model Construction
### Problem Statement and Hypothesis  
Figure 2 illustrates an example of the CER situation on a freeway, including multiple resource and disaster sites. For the convenience of numerical analysis, this paper makes the following hypotheses:  
1. The road network area contains M resource sites \(S=\{S_{1}\ldots S_{M}\}\), N disaster sites \(A=\{A_{1}\ldots A_{N}\}\), and K types of EVs \(G=\{G_{1}\ldots G_{K}\}\).
2. No more than K types of EVs are required at each disaster site.
3. Resource sites shall not be allocated resources over their reserves, and disaster sites shall not receive fewer resources than needed.
4. The distance between each resource site and the disaster site, the disaster site's response level, and the vehicle dispatch cost are known.
5. The average arrival time of EVs denotes the rescue waiting time.
# A Multichannel, High-Bandwidth Wirelane Receiver for D2D Interconnects
## 3 Receiver Circuits
### Mini-Pll
#### 3.3.2 Pfd  
The structure of the frequency and phase discriminator in this paper is shown in Figure 11a, which is mainly composed of two D flip-flops with reset function, delay module, logic operation and gate. The reference clock \(f_{ref}\) and the feedback signal \(f_{div}\) are used as the CLK end of the D starting device after passing through the buffer of the invertor. When there is frequency difference or difference between the two input signals, QA\({}_{-}\) P and QB\({}_{-}\)P will output high level, and the power supply pump will work. When two input signals are synchronized, both QA\({}_{-}\)P and QB\({}_{-}\)P outputs are low. QA\({}_{-}\)N and QB\({}_{-}\)N are the inverse output signals of QA\({}_{-}\)P and QB\({}_{-}\)P, respectively.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**P\textless{}2:0\textgreater{}** & 000 & 001 & 011 & 010 & 110 & 111 & 101 & 100 \\ \hline
**Ph**[\({}^{\circ}\)] & 0–45 & 45–90 & 90–135 & 135–180 & 180–225 & 225–270 & 270–315 & 315–360(0) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Higher three bits coding to P\textless{}2:0\textgreater{}.  
Figure 10: Architecture and design of Mini-PLL.  
Figure 11b is the working sequence diagram of the frequency and phase detector. When the reference clock \(f_{ref}\) leads, the QA_P signal jumps before the QB_P signal and becomes high level. At this time, after QA_P and QB_P phase are connected, the Reset signal becomes high level and is transmitted to two D triggers with a certain delay, so that QA_P and QB_P are brought back to low level. When the feedback signal \(f_{div}\) is in the lead, QB_P jumps before QA_P, becomes high level, and finally becomes low level under the action of the Reset signal.
## 3 Results and Discussion
### Current Efficiency of Metal Dissolution  
Figure 3 shows the current efficiencies at different current densities in the mixed solutions of NaNO\({}_{3}\) and NaOH. The concentration of the NaNO\({}_{3}\) solution was \(106\) g/L, and the concentration of the mixed NaOH solution ranged from \(1\) to \(30\) g/L. The variation tendencies of the measured current efficiency are strongly dependent on the concentration of the hydroxyl. For the \(106\) g/L NaNO\({}_{3}\) solution, a downward trend for the current efficiency was found with increasing current density. The current efficiency at low current density up to \(3\) A/cm\({}^{2}\) is much larger than that at higher current density, indicating serious stray corrosion occurring even at very low current density.  
Figure 2: Potentiodynamic polarization curves of Inconel 718 in NaNO\({}_{3}\), NaOH and a mixture of both.  
In contrast, by adding a small proportion of NaOH into the NaNO\({}_{3}\) solution, the variation tendency changed dramatically. The current efficiency can be reduced to 0 at 0.2 A/cm\({}^{2}\) for a mixed solution of 106 g/L NaNO\({}_{3}\) and 1 g/L NaOH. This means the anodic dissolution can be completely eliminated at small current density up to 0.2 A/cm\({}^{2}\), which is favorable to localizing the anodic dissolution. When the current density was over 0.2 A/cm\({}^{2}\), the current efficiency increased rapidly with increasing current density and reached a large value of 0.62 at 1.6 A/cm\({}^{2}\). The value of 0.2 A/cm\({}^{2}\) is generally defined as the cut-off current density. For higher concentrations of NaOH, a rightward shift can be observed for the ascending curve. When a high proportion of 30 g/L NaOH was used, the cut-off current density could be increased to 7.5 A/cm\({}^{2}\). The higher cut-off current density indicates a superior localization effect for the anodic dissolution in ECM.