esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulated Result and Analysis
### Evolution Design  
Six different cases of antenna elements based on rectangular feed elements (Case I, II, III) and Wang-shaped feed elements (Case IV, V) are discussed, as displayed in Figure 3a. With the rectangular feed element, Case I has a ring resonator on the metal rim, Case II has two ring resonators on the metal rim, and Case III has three ring resonators on the metal rim. The adoption of the SRR structure and Wang-shaped feed element made the width of the antenna element only 6.8 mm. Moreover, it can be found that a wide bandwidth can be obtained by a Wang-shaped feed element and SRR structure on the frame. As displayed in Figure 3b, the \(-10\) dB bandwidth of the proposed miniature eight-port antenna array can cover 3.3-5 GHz.  
Figure 2: The simulated coefficients of the proposed wideband eight-element antenna array. (**a**) Reflection coefficients. (**b**) Transmission coefficients.
## Appendix A
### Information Access  
In Remark 1, we mention that it suffices for all information regarding the input graph to be stored classically, with quantum access to it. Here, we expand on what we mean by that and refer the interested reader to [22] for further details.  
While our algorithms use quantum subroutines, the problem instances and their solutions are encoded as classical information. The required quantum access implies the capability to interact with the classical data in a way that allows computation in the superposition of that data. For instance, in the standard (non-distributed) Grover search algorithm, with a problem instance described by a function \(g:X\to 0,1\), we need the ability to apply the unitary \(U_{w}|x\rangle=(-1)^{g(x)}|x\rangle\) to an \(N\)-qubit superposition state \(|s\rangle=\frac{1}{\sqrt{N}}\sum_{x=0}^{N-1}|x\rangle\). This unitary is also referred to as the \"oracle\", and the act of calling it is called a \" \"query\". If we wish to use the distributed Grover search in Example 1, in which the node \(u\) leading the search attempts to determine whether each edge \(uv\) incident on it is part of a triangle in graph \(G\), the unitary that node \(v\) must be able to evaluate is the indicator function of its neighborhood, and \(u\) must be able to apply the Grover diffusion unitary restricted to its neighborhood. Then after initializing the \(N\)-qubit equal superposition, nodes \(u\) and \(v\) can send a register of qubits back and forth between each other, with \(v\) evaluating the unitary corresponding to the indicator of its neighborhood and \(u\) applying the Grover diffusion operator restricted to its neighborhood. The same ideas transfer over to a distributed quantum implementation of the EvaluationA or EvaluationB procedures. There, instead of evaluating unitaries corresponding to indicators, in step 2, each node \(v_{(i,j)k}\) evaluates the unitary corresponding to the truth values of inequality 3 for the evaluation steps. That information is then returned to the node that sent it, which can then apply the appropriate Grover diffusion operator.  
In general, quantum random access memory (QRAM) is the data structure that allows queries to the oracle. We can use circuit QRAM in our protocols or could make use of special-purpose hardware QRAM if it were to be realized. This choice does not affect the number of communication rounds but would affect the computation efficiency at each node. A main component of the distributed algorithms discussed in this work is the provision of quantum query access for each node to its list of edges and their associated weights in a given graph \(G\). This information is stored in memory, and the QRAM that implements the query to retrieve it can be accessed in \(\mathcal{O}(\log n)\) time, resulting in a limited overhead for our algorithms. This retrieval of information takes place _locally_ at each node; hence, this overhead does not add to the round complexity of our algorithms in the CONGEST-CLIQUE setting. We refer to [23] for more details on QRAM.
# Reflection Propagation Law of Electromagnetic Waves in U-Shaped Roadway
## 3 U-Shaped Roadway Model and Reflection Path Validity
### Valid Mirror Points
#### 3.1.2 Mirror Point Evaluation  
According to the principle of the mirror method, to determine the position of the reflection line, it is necessary to connect the mirror point with the receiving point. The point where the line intersects the reflecting surface is the reflection point, which is located on the reflecting surface. When the reflection point is valid, the mirror point is also valid. The scene applied in this study is a U-shaped roadway; therefore, when the mirror points at all levels are valid, the corresponding valid reflection lines can be obtained. There are two corners and a relay node \(P\) between the transmitting point T and receiving point \(R\). Unlike the propagation mode of a straight roadway, the rays emitted at the transmitting point are blocked by any of the planes \(m_{4}\), \(m_{5}\), and \(m_{6}\), which causes them to become invalid reflection lines. This is illustrated in Figure 3.  
Figure 3: Valid and invalid reflection lines. (**a**) Valid reflection lines; (**b**) invalid reflection lines over planes \(m_{4}\) and \(m_{5}\); (**c**) invalid reflection lines over planes \(m_{5}\) and \(m_{6}\); (**d**) invalid reflection lines over planes \(m_{4}\), \(m_{5}\), and \(m_{6}\).  
For the U-shaped roadway, a valid reflection point must satisfy the following conditions:  
1. The reflection point is located on the line connecting the mirror and reception points and intersects with the reflection plane (except for the boundary of the reflection plane).
2. The lines connecting the reflection point to the transmitting point, the reflection point, the relay node, and the receiving point cannot be obscured by the plane \(m_{4}\), the plane \(m_{5}\), and the plane \(m_{6}\).
3. When propagating in a straight roadway, the reflection point is valid if conditions a and b are satisfied.
4. The ray emitted from the emitting point needs to pass through the relay node, from which it is emitted to reach the reflecting surface, and the reflecting point is valid if condition a and condition b are satisfied.
## 2 Description of the Classification Method  
As depicted in Figure 1, the classification method mainly contains several steps, i.e., preprocessing, feature extraction, Residual Network training, and embedding layer design. Note that the underwater acoustic waves have different time duration, therefore, the preprocessing steps of truncation and framing are necessary. Afterward, we perform the feature extraction process, which contains feature calculation, feature fusion, and feature augment, to obtain the three-dimensional feature. Instead of using the general structure of CNN or DNN, we design an 18-layer Residual Network named \"ResNet18\" together with the center loss function. As a complex network, ResNet18 could learn very informative presentations in the training process. To avoid overfitting, we adopt the tactic strategy to train the aggregated features with early stopping and adaptable learning rate. Besides, aiming to increase the distance for inter-class and decrease that for Intra-Class,the embedding layers are well-tuned where the center loss function and softmax is used instead of the softmax.
## 1 Introduction
Thus, based upon the above discussion, it seems that SC and AI techniques are able to solve problems related to PPV prediction. It is true that many studies have been carried out for the purposes of estimating the PPV value; however, the literature still lacks comprehensive research focusing upon all combinations and correlations of variables that have effect on the estimation of PPV. Gene expression programming (GEP), among all the SC and AI techniques, is one of the models capable of forming proper equations applicable to the prediction and solution of potential problems. GEP is widely applied to various applications of geotechnical engineering [65; 66; 67; 68; 69]. The present paper tries to develop an equation based on GEP to predict PPV values. The developed equation is then applied as an input to the Monte Carlo (MC) technique. MC is known as a prevailing technique in probability simulation, which can be applied to a variety of fields such as civil engineering, construction management, geotechnics and risk assessment [70; 71]. The aim of this paper is to analyse the risk of ground vibration resulting from blasting by simulating all combinations of the effective variables in the PPV prediction, considering these variables' correlations. The remainder of this paper is structured as follows: Section 2 presents data that have been gathered, together with the methods used for the analyses. Then the results and discussion are presented in Section 3, and the paper is concluded in Section 4.
# Vendor-Agnostic Reconfiguration of Kubernetes Clusters in Cloud Federations
## 4 Analysis of the Feature Compatibility Problem
### Configuration of Network Plugins  
In Kubernetes, the container-level network is setup by an external network plugin that must conform with the CNI specification. However, in GKE or AKS, the Kubernetes' initial networking solution, called kubenet,ref-url9 is used as default, which only considers container connectivity within a node and relies on the cloud provider's infrastructure for cross-node routing. However, to use more advanced network features such as fast network policy enforcement via eBPF, CNI-based network plugins are required. In GKE or AKS, however, CNI plugins can only be activated through proprietary higher-level customization interfaces. Moreover, in none of the vendors it is possible to choose from the wide range of existing CNI plugins. Instead, only one vendor-specific CNI plugin can be activated. The following K8s features are affected:  
1. External Source Network Address Translation (sNAT) for Pod IPs. IP addresses of Pods cannot be routed to from outside of the cluster. With sNAT enabled, it becomes possible to connect to Pods from outside the cluster via stable external IP addresses;
2. Network policies. The K8s networking model requires that every worker node and pod must be able to connect to any other Pod IP address. Network policies allows expressing distributed firewall rules across the cluster to properly segment different applications from each other. This feature is only supported by some CNI plugins and there exists various implementations that differ in performance overhead and types of network policies;
3. Multiple network interfaces per Pod. This is only supported by the Multus CNI plugin;Reimplementation of the kube-proxy with more efficient load balancing at the level of the Linux kernel and reduction of the number of hops across nodes when forwarding requests to and returning responses from pods;
* Encryption of control plane and data plane messages.  
Note that when the control plane is hidden, the CNI plugin can only be changed at the worker nodes and not in the control plane. Although most control plane communication occurs via the node network instead of the Pod-level network, some functionalities such as probing of liveness and readiness of Pods requires controllers in the control plane to interact via the Pod network. Section 5.3 presents the vendor-agnostic reconfiguration tactic to replace network plugins while ensuring Pod connectivity with the control plane.
## 3 Results and Discussion
### Fluorescence Imaging of the Cys-AuNCs in the _E. coli_  
The fluorescence quantum yield of the Cys-AuNCs was measured to be 7.2% by employing an integrating sphere. To evaluate the application of the Cys-AuNCs as a fluorescence probe in the _E. coli_, the confocal microscope images of the _E. coli_ were examined after incubation with the Cys-AuNCs. As shown in the confocal images of Figure 7, Hoechst 33342 was first applied to stain the _E. coli_. The blue pseudocolor was observed from the rod-shaped _E. coli_. For the merged image, the fluorescence of Hoechst 33342 was homogeneously distributed in the _E. coli_. Moreover, for the control experiment, there was no detectable fluorescence from the _E. coli_. In comparison with the incubation of the _E. coli_ with the Cys-AuNCs, the red pseudocolor was observed from the rod-shaped _E. coli_, as shown in the merged image of the _E. coli_ incubated with the Cys-AuNCs. The results of the confocal microscope images indicated that the biocompatible Cys-AuNCs could be a promising fluorescent probe for bioimaging application.  
Figure 6: EPR spectra of the (**a**) _E. coli_ solution, (**b**) Cys–AuNCs, and (**c**) Cys–AuNCs.  
Figure 7: Confocal microscopic images of the _E. coli_. The blue and red pseudocolors represent the fluorescent signals of Hoechst 33342 and the Cys–AuNCs, respectively. In the control experiment, only sterilized water was added to the _E. coli_. The scale bars represent 2.5 \(\upmu\)m.
###### Abstract
Recently, [PERSON] and colleagues [21] investigated pulsed 30-90 GHz radiation penetration into the human ear canal and tympan membrane and concluded that there is very low penetration of RF radiation into the ear canal at 30 GHz and that, in this specific exposure condition, it is not likely to have a significant thermal impact on the tympanic membrane. [PERSON] et al. [22] examined RF exposure and absorption in ex vivo bovine brain tissue and a brain simulating gel at three frequencies (1.9 GHz, 4 GHz, and 39 GHz) that are relevant to 4G and 5G spectra. They found that the radiation penetration in the brain tissue, and therefore SAR, decreases with increasing depth and frequency. Despite the extensive literature on the subject [8; 10; 23; 24; 25; 26], there has been long-standing controversy about whether children absorb more RF energy than adults when using a smartphone [27]. Using realistic virtual human models, it is possible to find any differences in the exposure for different users such as adults vs. children. Moreover, different representative and usual scenarios should be considered, beyond the classic phone call scenario, to have a wider knowledge of the interaction between mmW and human tissues. The main purpose of this study is a numerical analysis of the local SAR distribution within different realistic 3-D human models exposed to the electromagnetic field of a mobile-communication antenna at the frequency of the latest generation of mobile networks 5G. We calculated, using the FDTD method, the local SAR in different exposure conditions by simulating three real common scenarios (phone call scenario, message writing scenario, and browsing scenario) regarding the use of smartphones/tablets by four different individuals (adult man and woman, child male and female).
# Resilience Assessment for the Northern Sea Route Based on a Fuzzy Bayesian Network
## 3 Model Established for NSR Resilience Measurement
### Resilience Measurement Using the Fuzzy Bayesian Network  
In this section, a Bayesian network is established to quantify the resilience of the Arctic Northeast Route security system. Based on the identified disruptions and the resilience capacity decomposition in Sections 3.2 and 3.3, the visual structure of the proposed Bayesian Network described in Figure 8 is developed using GeNle 2.3 software.
## 6 Engineering Application  
To validate the practical applicability of the road mechanical characteristic analysis method and computational program proposed in engineering, we conducted field measurements and data analysis of FWD deflection on the Shanghai-Shanxi Expressway. The road structure type of the engineering application section is as follows: 4 cm asphalt concrete surface layer + 5 cm asphalt concrete intermediate layer + 6 cm asphalt concrete subbase layer + 50 cm cement stabilized crushed stone base layer + 150 cm subgrade. In the engineering application section, calculations and analyses are solely conducted on the dynamic deflection of asphalt pavements under actual temperature and humidity conditions. The assumed conditions are as follows: a pavement temperature of 20 degrees, a subgrade volume moisture content of 11.5%, and a groundwater level 8 m below the subgrade surface. The measured load-time curve is shown in Figure 11.  
Figure 11: Measured load-time curve for the FWD.  
Figure 10: Impact of groundwater table on road surface deflection.  
In order to demonstrate the accuracy of the proposed calculation program under the influence of groundwater, the calculated deflection values will be compared with the measured deflection values based on the measured load-time curve and sensor positions. The related analysis results are shown in Table 4.  
From the test results under the influence of groundwater environment, it can be observed that the trend of the calculated theoretical deflection curve using the finite element method was generally consistent with the measured deflection curve. The correlation coefficient between the two was greater than 0.9. The maximum absolute value of the relative error between the theoretical deflection peak and the measured deflection peak was 6.27%, while the average absolute value of the relative error was 4.92%. Considering the influence of humidity factors, these errors are within acceptable limits for practical engineering calculations and analysis.
# Secure Cooperative Routing in Wireless Sensor Networks
## 4 Discussion
### Comparative Analysis
#### 4.2.1 Network Energy Consumption  
For MS-LEACH, when the nodes are less, almost 30% the energy consumption is around 95%. With the increase in the number of nodes, the energy consumption process increases and comes closer to the extreme value which is 100%, as shown in Figure 5. The blue dotted line is a reference paper technique which is the LEACH-based IDS mechanism, and the extreme energy consumption value for this is also 100%. The proposed MIDS has 43% less energy consumption with a change in the size of the network.  
The efficient use of energy by the BS makes the proposed algorithm use less energy. The fuzzy c-mean clustering algorithm decides the best possible nodes as CHs which also helps in the minimal use of power by the sensor nodes. Also, the AOMDV mechanism helps in minimal energy consumption by selecting the optimal and secure path for packet transmission.  
The MIDS algorithm keeps track of each node, and as soon as it finds an adversary, it generates an alarm and isolates that node. This quick response mechanism helps in the minimal loss of energy by the attacker nodes. In Figure 10, at the start, the energy consumption of MIDS is as low as 35%, but with the increase in the size of the network it starts increasing but the extreme value for this algorithm is not more than 55%.
# Analysis of Cyclone Spinning Effect with Different Guide Vane Heights
###### Abstract  
In order to explore the influence of change in the structural parameters ofguide vane cyclones on the cyclone spinning effect, this paper mainly used numerical simulations and physical experiments to analyze the energy of the hydrodynamic flow of acyclone with different guide vane heights by taking the structuralparameters of the guide vane height as the research object. The results show that the rotational kinetic energy of the water flow inside the cyclone was almost zero in the upstream and straight sections of the guide vane section, and it only existed in the leading edge section of the guide vane. In the twisted section of the guide vane, the rotational kinetic energy increased along the flow path, while it decreased in the downstream section of the guide vane. An increase in the height of the guide vanes led to an increase in local mechanical energy loss at the leading and trailing edges of the guide vanes of the cyclone. In the guide vane section, the mechanical energy loss of the water flow remained almost constant along the path, but the mechanical energy loss was faster for cyclones with greater heights. During the deflection of the guide vane, pressure energy was converted into kinetic energy, and the higher the height of the guide vane, the greater the kinetic energy growth and mechanical energy consumption. The proportion of additional mechanical energy loss in the total loss increased with the increase in guide vane height, and the influence of guide vane height was greater than that of the Reynolds number. The mechanical efficiency ndecreased with the increase in guide vane height, whereas the mechanical efficiency increased slightly with the increase in Reynolds number. The research results in this paper provide a theoretical basis for further optimizing the structural parameters of cyclones.  
cyclone; spinning effect; mechanical efficiency +
Footnote †: journal: Applied sciences
# Robust U-Nets for Fetal R-Peak Identification in Electrocardiography
## 4 Evaluation Results of Proposed Method
### Robustness
#### 4.2.2 Evaluation on Synthetic Recordings  
To further evaluate the method's robustness, we conducted experiments on synthetic data from the FECGSYN database, including the original dataset and three noise-corrupted versions with signal-to-noise ratios (SNRs) of 6 dB, 9 dB, and 12 dB (denoted as SYN_origin, SYN_6, SYN_9, and SYN_12, respectively). For each dataset, we performed 5-fold cross-validation with 40 records from two patients are used for training and 10 records from two additional patients for testing. With the post-processing threshold fixed at \(Thr_{1}=0.3\), the average performance metrics of the U-Net model across different SNR levels are presented in Table 4. The proposed method demonstrates strong detection accuracy on SYN_origin, obtaining 99.96% PPV, 99.93% SEN, and 99.94% F1-score. It also maintains a score of over 85% on the SNR of 9 dB and 12 dB conditions. However, performance degrades significantly under high noise conditions (6 dB SNR), with the average F1-score dropping to 58.12% across all 50 test records.
# Building Realistic Mobility Models for Mobile Ad Hoc Networks
## 6 Real Shipping Movement Model
### Shipping Model  
To reiterate, data files are obtained from ShipAIS ([PERSON], nd, [194]), which logs and displays ship movements for the area of focus. This publicly available trace data is for all AIS traffic for a 24-hour period within this designated area. The relevant records are processed to extract vessel maritime mobile service identity (MMSI), location, and speed. This data is converted into a realistic working mobility model for use and format required by ns-2. The area covered and the time of simulation are taken from the trace data and hard coded in a TCL script (for details, see [PERSON], 2014, [2]), while the number of nodes is extracted from the AIS data during processing. However, the AIS data is not entirely reliable, because ships can switch their AIS on and off, so they can appear or disappear at any time (though, this usually indicates that they are starting or stopping). Although ships have a legal obligation to transmit AIS data, technical issues can be a barrier. Due to the limitations of the marine VHF radio used and the limited number of receivers supplying the data, there are also black spots where data is not available. This means that ships can seem to jump from one location to another. Two solutions have been applied to address this problem. Ignoring these anomalies when producing the ns-2 movement file results in ns-2 interpolating the paths of ships that disappear and then reappear. This produces movements that are realistic in terms of ship speed but can also result in ships apparently sailing across land. Checking for significant time or location gaps in a ship's AIS record enables the ship to be removed from the model when it disappears from the record. However, this ship is still in the real world and could potentially contribute to the MANET. The results presented here are based on interpolation of movement where ships disappear and then re-appear on the record. A snapshot of the nodes' locations is shown in Figure 11.