esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
A novel antenna structure is constructed from cascading multi-stage metamaterial (MTM) unit cells-based printed monopole antenna for 5G mobile communication networks. The proposed antenna is constructed from a printed conductive trace that fetches four MTM unit cells through four T-Resonators (TR) structures. Such a combination is introduced to enhance the antenna gain-bandwidth products at sub-6 GHz bands after exiting the antenna with a coplanar waveguide (CPW) feed. The antenna circuitry is fabricated by etching a copper layer that is mounted on Taconic RF-43 substrate. Therefore, the proposed antenna occupies an effective area of 51 \(\times\) 24 mm\({}^{2}\). The proposed antenna provides an acceptable matching impedance with S\({}_{11}\)\(\leq\) -10 dB at 3.7 GHz, 4.6 GHz, 5.2 GHz, and 5.9 GHz. The antenna radiation patterns are evaluated at the frequency bands of interest with a gain average of 9.1-11.6 dBi. Later, to control the antenna performance, four optical switches based on LDR resistors are applied to control the antenna gain at 5.85 GHz, which is found to vary from 2 dBi to 11.6 dBi after varying the value of the LDR resistance from 700 \(\Omega\) to 0 \(\Omega\), in descending manner. It is found that the proposed antenna provides an acceptable bit error rate (BER) with varying the antenna gain in a very acceptable manner in comparison to the ideal performance. Finally, the proposed antenna is fabricated to be tested experimentally in in free space and in close to the human body for portable applications.  
M 2023, 14, 131. [[https://doi.org/10.3390/mi14010131](https://doi.org/10.3390/mi14010131)]([https://doi.org/10.3390/mi14010131](https://doi.org/10.3390/mi14010131))  
1  
T.A.; [PERSON] A Printed Reconfigurable Monopole Antenna based on a Novel Metamaterial Structures for 5G Applications
## 5 Conclusions
\begin{tabular}{l l}
**Symbols** & **Meanings** \\ \(\varepsilon\) & Strain tensor \\ \(\kappa\) & Curvature tensor \\ \(u\) & Macroscale displacement vector \\ \(\omega\) & Macroscale rotation vector \\ \(v\) & Particle translation vector \\ \(\omega\) & Particle rotation vector \\ \(\mathbf{A}\) & Micro-strain tensor \\ \(\mathbf{B}\) & Micro-curvature tensor \\ \(\mathbb{A}^{3}\) & The gradient of micro-strain \\ \(\mathbb{B}^{3}\) & The gradient of micro-curvature \\ \(\rho\) & Macroscale density \\ \(\rho_{m}\) & Microscale (particle) density \\ \(V^{\prime}\) & The volume of unit cell \\ \(\alpha\) & Second order moment of inertia \\ \(\beta\) & Second order polar moments of inertia \\ \(\tau\) & Relative Cauchy stress tensor \\ \(\upsilon\) & Relative couple stress tensor \\ \(\Phi\) & Double body force tensor \\ \(\mathbf{\Upsilon}\) & Double surface traction tensor \\ \(\Pi\) & Double body moment tensor \\ \(\Gamma\) & Double surface moment tensor \\ \(\mathbf{K}\) & Contact linear stiffness tensor \\ \(\mathbf{G}\) & Contact rotation stiffness tensor \\ \(\mathbf{I}\) & Second order identity tensor \\ \(\mathbf{\delta}\) & Kronecker tensor \\ \(\zeta\) & The probability density function of contact distribution \\ \(a\) & Anisotropy parameter \\ \(\mathbb{C}^{4},\mathbb{E}^{4},\mathbb{M}^{4}\) & 4 th order constitutive tensors \\ \(\mathbb{H}^{6},\mathbb{N}^{6}\) & 6 th order constitutive tensors \\ \(k\) & Wave number \\ \(V\) & The volume of material point \\ \(S\) & The surface of material point \\ \(\mathbf{R},\mathbf{D}\) & Relative deformation tensors \\ \(\mathbb{R}^{3},\mathbb{D}^{3}\) & Higher-order relative deformation tensors \\ \(\Delta\) & Relative displacement at contact \\ \(\theta\) & Relative rotation at contact \\ \(I\) & Branch vector \\ \(\mathbf{L}\) & Internal length tensor \\ \(T\) & Kinetic energy \\ \(W\) & Deformation energy \\ \(Q\) & The work done by external forces \\ \(\sigma\) & Cauchy stress tensor \\ \(\mu\) & Couple stress tensor \\ \(\widetilde{n}\) & Unit outwards-pointing normal vector of the boundary surface \\ \end{tabular}
# Coordinated Control of Virtual Power Plants to Improve Power System Short-Term Dynamics
## 1 Introduction  
### Motivation  
A Virtual Power Plant (VPP) is obtained by aggregating the capacity of several Distributed Energy Resources (DERs), Energy Storage System (ESS), and dispatchable loads [1]. It operates as a virtual transmission-connected generator in the existing power system [2]. For operation purposes, the active power output of a VPP is scheduled similarly to conventional generators, e.g., through the solution of a daily ahead unit-commitment problem [3]. In transient conditions, e.g., following a contingency, VPPs must provide frequency support [4]. The active power scheduling and the frequency control are generally decoupled due to their different time scales. Instead, this paper proposes to combine these functions by switching to coordinated control of the DERs and ESSs that form the VPP during the contingency.
# RTK with the Assistance of an IMU-Based Pedestrian Navigation Algorithm for Smartphones
## 2 Materials and Methods
### Data Collection
#### 2.5.1 GNSS Raw Measurements' Collection  
Here, we used an app called Geo++ RINEX Logger, which could produce a file of measurements in RINEX format [49]. There are other similar apps such as RINEX ON [50]. RINEX ON has the advantage of providing not only measurements, but also ephemeris. However, not all kinds of smartphones provide the users with ephemeris. Google also released the GnssLogger app to log the data related to GNSS measurements [51]. GnssLogger did not directly provide pseudorange or carrier phase. Users could only calculate the pseudorange and carrier phase with the data provided by GnssLogger.  
Many other smartphones are equipped with BCM47755 and Android P. We can refer to the Android Developers Documentation to choose one kind of phone to develop some applications or test the RTK and PPP performance [52].  
Our experiments were based on the Geo++ RINEX Logger and the Xiaomi Mi 8. The Xiaomi Mi 8 equipped with the BCM47755 could utilize dual-frequency signals, as Figure 2 shows.  
Figure 2: Visualization of the various GNSS carrier frequencies.  
All the Galileo satellites can broadcast E1 and E5a signals. In contrast, only 12 GPS satellites whose Block types are IIFcan broadcast the L5 signals [53]. A website called Trimble GNSS Planning can be helpful to identify a period when there are the most visible satellites that broadcast L1 (E1) and L5 (E5a) signals in a day [54]. We chose an M300 receiver made by ComNav Technology Ltd. Shanghai, China as the base station. This receiver can provide the measurements of all frequencies and all constellations. We also chose a dual-frequency NovAtel receiver, which supports L1 and L2 signals, as another rover. The position results provided by the dual-frequency RTK using the M300 receiver and the NovAtel receiver were regarded as the references.
## 3 Material and Methods
### Application Case Study
#### 3.2.3 The Prediction Model  
Because of the way we will later explain predictions, we trained one model for each prediction in the outlier window and since we compare scores and explanations across songs, for each prediction in the outlier windows we trained one common model.  
As discussed in Section 2 LSTMs have good performance on time series prediction tasks. Since neural networks also fit well with the large amounts of training data, we trained an LSTM for each day in the outlier window.  
Songs have very different numbers of streamings so training data are normalized relative to the mean and standard deviation of the context windows (e.g., \(c_{train}\)). Likewise, when predicting a time series, it is normalized before the prediction is made and the result is denormalized before it is returned:  
\[D_{train}^{norm}=\frac{D_{train}-mean(c_{train})}{std(c_{train})} \tag{7}\]  
We want a network that will not learn the outliers (as perceived by the users, cf. Challenge 3) left in the training data, and at the same time, it should be good at predicting the inliers (Challenge 1). The amounts of data are an advantage and mean that it is possible to fit a complex normal behavior without fitting outlier behavior. The assumption here is that normal days are clustered in days of similar behavior, while outliers are either unique, within micro-clusters of similar behavior, or simply unpredictable. We have kept the neural network simple and added a dropout layer to regularize and increase robustness (Challenge 2).  
The final neural network (Figure 2) was selected iteratively as the network with the best outlier ranking and outlier explanations, judged by the users. The model is optimized to minimizing the mean absolute error (_mae_). Training with _mae_ instead of the usual mean squared error (_mse_) puts less effort in fitting outliers and will therefore contribute to solving Challenge 1. We train the model in 2000 epochs with a batch size of 32 using the Adam algorithm [32].  
In Section 4, we describe experiments with different configurations of the neural network and the training process.
# SDN Anomalous Traffic Detection Based on Temporal Convolutional Network
###### Abstract
In the field of anomaly detection, the layer expansion mechanism of TCN can effectively capture multi-scale timing features, and its fixed-length history window mechanism avoids the gradient propagation problem of traditional RNN. These characteristics enable TCN to demonstrate unique advantages in network traffic timing analysis. To address the above challenges, this paper proposes a method for anomalous traffic detection featuring packet length sequences, based on TCN model to learn the features. The TCN model employs a convolutional network to process time-series data, modeling temporal dependencies through causal convolution (ensuring that predictions at the current time step are not affected by future time steps) and dilated convolution (expanding the sensory field) to model temporal dependencies. The approach aims to improve the accuracy and efficiency of detection, reduce the false alarm rate, and enhance the generalization ability of the model to better cope with complex network environments and variable attack methods. This paper presents several significant contributions:  
* In this study, TCN is applied to the work of SDN traffic anomaly detection. The TCN model is able to effectively capture the long-range dependencies of the packet length sequences, and at the same time, it utilizes parallel computing to improve the training efficiency and overcome the inefficient training and gradient vanishing problems of the RNN model. * Different from the methods that rely on the traditional stream statistics features, this paper proposes that using the packet length sequence as the core feature representation can reduce the feature dimension while preserving the key features of the attack behavior and improve the detection accuracy. The five-tuple grouping policy (source IP address, destination IP address, source port, destination port, and protocol) is used to optimize the feature extraction mode, enhance the expression capability of traffic behavior features, and improve classification accuracy. * Experiments on public InSDN dataset show that the proposed method achieves high accuracy in normal traffic and malicious traffic classification tasks, and that it is superior to the baseline method in detection and computation efficiency. In summary, the anomaly detection framework proposed in this study provides a way for network anomaly detection. In the development process of 5G and subsequent technologies, the framework has shown potential application value in many fields, such as industrial Internet of Things [8], intelligent mission-critical services [9], network function virtualization [10] and virtualized network slicing environment [11]. The practical application effect of the framework in 5G and future technologies will be further explored and verified in the future. The rest of the paper is organized as follows. We present related work in Section 2. The methodology proposed in this paper is described in Section 3. The experiments are synthesized in Section 4. Finally, we conclude the paper in Section 5.
## 1 Introduction and Review of Existing Research
### Smart Energy Management Systems  
Smart energy management systems (SEMS) monitor, control, and automate energy systems based on sensor data, programmed optimisation algorithms, and connectivity to improve energy use. This contributes significantly to sustainable construction, the development of ZEB buildings, improved occupant comfort, and reduced operating costs.  
Incorporating sensors, smart meters, and other devices that collect real-time data is key to continuously monitoring building's energy use, as well as identifying patterns, inefficiencies and areas for improvement in terms of maintenance and operation. In this way, advanced control strategies, such as predictive maintenance and adaptive learning algorithms, can optimise the operation of heating, ventilation, and air conditioning (HVAC) systems to reduce energy consumption without compromising occupant comfort. For this reason, SEMS are programmed with algorithms and machine learning (ML) techniques that intervene to dynamically adjust the necessary variables according to energy consumption patterns or to avoid cost overruns generated by anomalies. Proper management can detect faults and inefficiencies early, enabling predictive maintenance and reducing the likelihood of costly equipment failures, which in turn prolongs equipment life and reduces maintenance costs.
## 7 Numerical Properties of CVM  
The stability amplification factor \(\sigma\) is used to enlarge the unconditional stability range of CVM. The application of this parameter is studied further for the other numerical properties of CVM. The evaluation details are referred to from references and they will not be elaborated in this paper.
## 3 Denoising Diffusion Probabilistic Model (DDPM)
### Advantages of Using DDPM in Biomedical Image Segmentation
#### 3.4.2 Probabilistic Modeling for Uncertainty Estimation  
DDPM inherently adopts a probabilistic modeling approach, allowing it to capture uncertainty in the data distribution. This is particularly advantageous in biomedical image segmentation, where uncertainty estimation is crucial for assessing the reliability of segmentation results. Bayesian interpretations of DDPM provide valuable uncertainty measures that can inform clinicians about the confidence levels associated with segmentation outcomes [43; 53].
## 4 Experimental Result
### Dataset Generation
However, due to the SDN characteristics that only forward a packet with a unique IP address towards the controller, only 63 or 61 packets are forwarded to the controller. Consequently, using Equation (4), the attack traffic ratio within a 60 min window for single or multiple hosts' low-rate DDoS attacks is 7% and 19%, respectively. Moreover, the \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Description** & **Number of Normal Packets in the Traffic** & **Number of Attack Packets in the Traffic** & **Attack Traffic Ratio** \\ \hline Dataset 1 & LSS & 226,800 & 18,000 & 7\% \\ Dataset 2 & HSS & 226,800 & 118,800 & 34\% \\ Dataset 3 & LSM & 226,800 & 18,000 & 7\% \\ Dataset 4 & HSM & 226,800 & 118,800 & 34\% \\ Dataset 5 & LMS & 219,600 & 54,000 & 19\% \\ Dataset 6 & HMS & 219,600 & 356,400 & 62\% \\ Dataset 7 & LMM & 219,600 & 54,000 & 19\% \\ Dataset 8 & HMM & 219,600 & 356,400 & 62\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Total number of normal and attack traffic packets for eight different scenarios. attack ratio within the 60 min window for single or multiple hosts' high-rate DDoS attacks is 34% and 62%, respectively. \[ATTack\ Traffic\ Ratio=\frac{Attack\ Packet}{packet\ total}\times 100\% \tag{4}\]  
where \(Attack\ Packet\) denotes the number of attack packets and \(Packet\ Total\) represents the total number of packets generated throughout the simulation. This work utilizes a new dynamic threshold based on the Renyi joint entropy values and proposes a rule-based detection mechanism to detect low- and high-rate DDoS attacks. Figure 3 illustrates the flowchart of the proposed approach. As shown in Figure 3, the proposed approach consists of three core steps: (i) calculation of Renyi joint entropy to calculate the network traffic randomness, based on \((H_{RJa}(x,y)_{i})\); (ii) calculation of the dynamic threshold (\(Th\)), based on \(H_{RJa}(x,y)_{i}\); and (iii) rule-based detection to detect DDoS attacks, based on \(Th\) and \(H_{RJa}(x,y)_{i})\).
# Efficient Route Management Method for Mobile Nodes in 6 TiSCH Network
## 3 Efficient Route Management Method for Mobile Nodes in 6 TiSCH Network  
When a mobile node joins the network to build a routing path, we present a technique for minimizing control message conflicts and the latency from the root node to the root node in response to packets requested by the root node. This technique also minimizes the latency of transmitting responses to packets requested by the moving node from the root node to the moving node through the root node fixed path construction technique. In addition, a technique is presented that allows faster path formation of a moving node by minimizing the conflict of control messages when the moving node disconnects the link to the preferred parent node through the moving node's fast parent selection technique.
## 2 Full-Scale Tests for Tunnel Boring Machine (TBM) Tunneling
### Stepwise Tunneling, Based on Operational Parameters of TBM  
A variety of test cases were planned and executed by performing the TBM advance in around 80 cases achieved by the variation of four rock mass strength values and 20 TBM operational parameters. Thrust force and rotational speed of the cutterhead were selected as the main TBM operational parameters in this study. For each UCS case, extensive results were obtained by operating the shield TBM at five thrust force levels of 1000 kN, 2000 kN, 3000 kN, 4000 kN, and 5000 kN generated by the machine, as well as at four different cutterhead rotational speeds of 1.5 RPM, 4.5 RPM, 6.0 RPM, and 7.5 RPM. The TBM driving was performed stepwise so that 20 sets of operational conditions given in Table 3 could be developed. However, the application of operational parameters altogether sometimes presented certain limitations in specific situations such as the partial cutterhead drive capacity at low UCS of rock mass (due to higher torque development, as explained in Section 3.2) as well as the TBM rolling problem operated at lower rotational speeds of the cutterhead. This can be further understood by considering Test# 1 as an example, where the application of 4000 kN or 5000 kN thrust force resulted in high penetration depth and caused the development of high torque value. Therefore, it can be deduced that the development of high rotational speed of the cutterhead could not be possible sometimes, within 630 kW of power. Stepwise driving refers to the conditions in which thrust force and RPM were kept constant in each test case. For example, the constant conditions of 2000 kN thrust force and 6.0 RPM were kept in operation in order to achieve the excavation within a range of 70 mm to 100 mm. The shield jacks were paused for a short time after each set of tests, and the specific RPM for the next set of the test was adjusted by changing the number of hydraulic pumps being used. In order to satisfactorily achieve the target RPM levels, the selection of number of hydraulic pumps being used was decided by trial and error method according to different UCS values.  
Figure 5: View of artificial rock mass and rock box at various stages, (**a**) during preparation of artificial rock mass in rock box; (**b**) view of the rock box after tests.  
\begin{table}
\begin{tabular}{l c} \hline
**Design Parameters** & **Cases** \\ \hline Thrust force(kN) & 1000, 2000, 3000, 4000, 5000 \\ RPM (rev/min) & 1.5 (1 pump, 90 kW), 4.5 (3 pumps, 20 kW), \\ (It can be varied according to torque-RPM relationships) & 6.0 (4–5 pumps, 360–450 kW), 7.5 (5–7 pumps, 450–640 kW) \\ \hline \end{tabular}
\end{table}
Table 3: TBM operational parameters applied in various test cases.
## 1 Introduction
These encompass essential functions such as mode conversion [24; 25; 26], focusing [27; 28], modulation [29; 30; 31; 32; 33; 34; 35; 36; 37], filtering [38; 39; 40; 41], and switching [32; 42; 43; 44; 45], which are essential for the development of faster and more efficient optical communication and data processing systems. In addition, plasmonic materials and structures can be used to create extremely compact light sources [46], including plasmon lasers [47; 48; 49] and nanoscale light emitters [50; 51; 52]. Plasmonics can enhance the capabilities of purely dielectric platforms thanks to their broadband emitter (with a shorter lifetime) helping in the design of integrated coherent sources of single photons [53] for quantum applications [54; 55; 56; 57]. However, two types of constraints limit the utilization of plasmons in optical functions: (1) the high optical losses induced by the interaction of the wave with the metal allow the use of plasmons only sparingly and (2) the excitation of plasmons from electromagnetic waves demands complex strategies, primarily due to their propagation constants, which are intrinsically higher, or even significantly higher, than those of electromagnetic waves in the surrounding environment. These two limitations can be simultaneously addressed through a smart integration of photonic circuits and plasmonic structures. In this case, embedding plasmonic structures on waveguides and photonic circuits [58] allows us to benefit from the advantages of both components, namely the low losses of dielectric waveguides and compactness of plasmons. In other words, such an integration allows us to insert plasmonic structures only where needed, and connecting them with other functions of the circuit thanks to low loss dielectric waveguides. Nevertheless, this integration poses a significant challenge due to the substantial differences in spatial extension between dielectric and plasmonic modes, which are accompanied by contrasting propagation constants. In this context, the purpose of this article is twofold. On the one hand, it reviews strategies for integrating plasmonic structures, which support localized or propagative plasmons, on dielectric waveguides. On the other hand, it demonstrates the diversity of potential applications where the synergistic integration of plasmonic features can lead to innovations.