esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis and Experimental Test of Electrical Characteristics on Bonding Wire
## 3 Frequency Domain Simulation
### Influence of Diameter on S Parameter of Bonding Wire  
Considering the density of the pins connection, the diameter of the bonding wire is an important design parameter. Six kinds of nominal diameters of bonding wire were selected for simulation: 15 \(\upmu\)m, 20 \(\upmu\)m, 25 \(\upmu\)m, 30 \(\upmu\)m, 35 \(\upmu\)m and 40 \(\upmu\)m. Taking the SP12 bonding wire as a model, the span of L was 4500 \(\upmu\)m and the connection height of H1 was 250 \(\upmu\)m. The other simulation parameters were kept constant, while the diameter of d was chosen as the variable. The sweep frequency ranges from 1 MHz to 20 GHz. The results of the S11 and S21 with the diameter and frequency of the bonding wire are shown in Figure 7a,b.  
Figure 7a is the logarithmic coordinate graph of S11. It can be easily seen that the return loss grows with increase of frequency. For example, the bonding wire with a diameter of 40 \(\upmu\)m can operate at the near 5 GHz frequency. Compared with the diameter of 15 \(\upmu\)m, the working frequency band increased by 17.5% (0.73 GHz,). Figure 7b is the logarithmic coordinate graph of S21. With the increase of frequency, the insertion loss rises, while the signal transmission performance decreases. The S21 extreme points of different diameters were extracted as shown in Table 3. When the diameter was 40 \(\upmu\)m (17.18 GHz), S21 had the minimum value of \(-\)8.16 dB, and the ratio of passing with incoming signal was 39.06%. Compared with the diameter of 15 \(\upmu\)m, the modulus of S21 went up by 1.26 dB (about 15.4%). Also, the signal transmission quality increased by 5.26%.
# A Wideband Eight-Element Antenna with High Isolation for 5G New-Radio Applications
## 4 Conclusions  
A wideband eight-element is presented in this paper. This MIMO antenna includes eight elements made up of a C-shaped strip and two triangle-shaped strips to produce the frequency bands of 5G NR N77 and N79. The measured isolation is larger than 18 dB. The radiation efficiency and ECCs are higher than 75% and lower than 0.005, respectively. The designed eight-element wideband antenna is an agreeable choice for 5G NR smartphone applications.  
Conceptualization, J.C.; methodology, G.L.; data curation, J.Z. and S.X.; investigation, J.H.; writing--original draft preparation, J.C.; writing--review and editing, G.L.; supervision and funding acquisition, G.L. All authors have read and agreed to the published version of the manuscript.  
This research work was partially funded by the National Natural Science Foundation of China under Grant No. 61671330, the Science and Technology Department of Zhejiang Province under Grant No. LGG19F010009, and Wenzhou Municipal Science and Technology Program under Grant No. C20170005 and No.2018 ZG019.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Operating Band (GHz)** & **Isolation (dB)** & **Total Efficiency (\%)** & **ECC** & **Size (mm\({}^{3}\))** \\ \hline
[1] & 4.37–5.5 & \(<\)\(-22\) & 40–50 & \(<\)0.068 & 15.6 \(\times\) 15.6 \(\times\) 1.6 \\
[5] & 3.25–5.93 & \(<\)\(-10\) & 41–69 & \(<\)0.1 & 13.9 \(\times\) 7 \(\times\) 0.8 \\
[15] & 3.3–4.2 & \(<\)\(-10.5\) & 63.1–85.1 & \(<\)0.3 & 30 \(\times\) 7.5 \(\times\) 2 \\
[17] & 3.3–5.95 & \(<\)\(-15\) & 47–78 & \(<\)0.11 & 17 \(\times\) 6 \(\times\) 0.8 \\
[19] & 3.08–5.15 & \(<\)\(-10\) & - & - & 39.75 \(\times\) 10.5 \(\times\) 1 \\
[20] & 3.3–5 & \(<\)\(-12\) & 31.6–88.6 & \(<\)0.11 & 40 \(\times\) 3 \(\times\) 7.5 \\ Pro. & 3.3–5.45 & \(<\)\(-18\) & \(>\)75 & \(<\)0.005 & 6.8 \(\times\) 7 \(\times\) 0.8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance comparison of the designed antenna with other published antennas.  
Figure 17: Computed CCL of the designed antenna.  
Data Availability Statement: The data supporting this research article are available upon request to the corresponding author.  
The authors declare that there is no conflicts of interests regarding the publication of this manuscript.
# A Survey of Deep Learning for Data Caching in Edge Network
## 2 Data Caching Review  
The paradigm of data caching in edge networks is illustrated in Figure 2. Similar to [10], the scope of edge in this paper is along the path between end user and data server, which contains Content Router (CR), Macro Base Station (MBS), Femto Base Station (FBS), and End Device (ED). In the context of Cloud-Radio Access Network (C-RAN) [11], both baseband unit (BBU) and remote radio head (RRH) are considered as potential caching candidates to hosting content, where the BBUs are clustered as a BBU pool centrally and RRHs are deployed near BS's antenna distributively. According to the hierarchical structure of edge network, the data caching is classified into two categories: Layer 1 Caching and Layer 2 Caching. In this section, we illustrate the typical research topics in these two areas.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Abbr.** & **Description** & **Abbr.** & **Description** \\ \hline
3C & Computing, Caching and Communication & A3C & Asynchronous Advantage Actor-Critic \\ \hline BBU & Baseband Unit & CCN & Content-Centric Network \\ \hline CNN & Convolutional Neural Network & CoMP-JT & Coordinated Multi Point Joint Transmission \\ \hline CR & Content Router & C-RAN & Cloud-Radio Access Network \\ \hline CSI & Channel State Information & D2D & Device to Device \\ \hline DDPG & Deep Deterministic Policy Gradient & DL & Deep Learning \\ \hline DNN & Deep Neural Network & DQN & Deep Q Network \\ \hline DRL & Deep Reinforcement Learning & DT & Digital Twin \\ \hline ED & End Device & ES & Edge Server \\ \hline ETSI & European Telecommunication Standardization Institute \\ \hline ESN & Echo-State Network & FIFO & First In First Out \\ \hline FNN & Fully-Connected Neural Network & FBS & Femto Base Station \\ \hline GRU & Gated Recurrent Unit & ICN & Information-Centric Network \\ \hline IoT & Internet of Things & LFU & Least Frequently Used \\ \hline LP & Linear Programming & LRU & Least Recently, Used \\ \hline LSTM & Long Short-Term Memory & MAR & Mobile Augmented Reality \\ \hline MD & Mobile Device & MILP & Mixed Integer Linear Programming \\ \hline MBS & Macro Base Station & NFV & Network Function Virtualization \\ \hline PNF & Physical Network Function & PPO & Proximal Policy Optimization \\ \hline QoE & Quality of Experience & RL & Reinforcement Learning \\ \hline RNN & Recurrent Neural Network & RRH & Remote Radio Head \\ \hline SAE & Sparse Auto Encoder & SDN & Software Defined Network \\ \hline Seq2 Seq Sequence to Sequence & SNM & Shot Noise Model \\ \hline TRPO & Trust Region Policy Optimization & TTL & Time to Live \\ \hline VNF & Virtual Network Function & WSN & Wireless Sensor Network \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of abbreviations.
# Analysis of Driving Behavior of Micromobility Vehicle Users at Mini-Roundabouts
## 2 Materials and Methods
### Recruitment of Participants  
A structured approach was used to select participants for a study to analyze the behavior of micromobile vehicles at mini-roundabouts. The study was conducted in compliance with the ethical guidelines and scientific framework approved by the DISS--Center for Road Safety at the University of Parma (Protocol No. 211117/2021, 24 February 2021). This research is in line with the overall objective of \"modeling the performance of autonomous vehicles on roundabouts through naturalistic investigations\", as stated in the approved protocol, while emphasizing micromobility, a critical and emerging aspect of urban transportation.  
For the study, an information stand was set up in the Katane shopping center near the roundabout under study, equipped with promotional materials and detailed information about the research objectives. Recruitment focused on people who already had experience with bicycles and electric scooters to ensure that participants were representative of typical micromobility users.  
The bicycles used for the tests were conventional, non-electric bicycles, chosen to represent the most common and accessible form of micromobility. The electric scooters provided to the participants were standard e-scooters, reflecting widely used models. This choice was made to allow a direct comparison between the use of a conventional bicycle and an electrically assisted form of micromobility, both of which are commonly found in urban environments.  
In line with DISS-approved ethical considerations, participants were fully informed about the aims of the study, how the data would be used, and how their privacy would be protected. Participation in the study was voluntary and not financially remunerated, reflecting adherence to the principles of the Declaration of Helsinki.  
When selecting participants, care was taken to ensure that they were diverse in terms of age, gender, and experience and represented the heterogeneous profile of micromobility users. The organization provided electric scooters and bicycles for the tests, although participants could also use their own vehicles. The final sample comprised 20 people, evenly split by gender, predominantly aged 18 to 30--a demographic segment known in the literature for its higher acceptance of micromobility.
# IoT Anomaly Detection to Strengthen Cybersecurity in the Critical Infrastructure of Smart Cities
## 3 Results
### Data Set Description  
The data set used in this study comes from an extensive collection of information within a smart city framework over six months. The data was generated from a network of IoT devices distributed throughout the city, designed to monitor and collect information in real-time on various aspects of critical infrastructure and the urban environment. During this period, more than five terabytes of data were recorded, representing a rich source of information for analysis and anomaly detection.  
The most relevant case study focuses on the city's smart electrical grid. In this context, anomaly detection models performed exceptionally well, identifying attempts to tamper with the power grid. These tampering attempts could have resulted in severe power outages or security risks. The models' ability to detect and alert to these threats contributes significantly to preventing cyberattacks and ensuring the integrity and reliability of the urban power grid. This specific case study highlights the importance of anomaly detection in critical environments, such as electrical infrastructure, and demonstrates the effectiveness of the models developed in this research.
## 2 Analytical Model and Equations
### Passive Control Theory  
The displacement of the tower top is affected by the lifting weight of the cable, and increases with the increase of span and section weight. The tower top displacement control also becomes more difficult. Traditionally, the manual operation of jacks is used as the passive control method to correct tower deflection. In fact, the tower deflection adjustment method is inefficient. Furthermore, with the traditional method, it is difficult to ensure that the load adjustment effect achieves the ideal state, and it is time-consuming and laborious, especially when the span of the arch rib increases, the corresponding number of arch rib segments increases, and each lifting segment needs to control the deviation of the tower top under a reasonable target state, which requires constant adjustment. Thereby, it is difficult for the existing adjustment method of tower top deviation to meet the construction control requirements.  
In the passive control proposed by [PERSON] et al. (2018), the constantly changing horizontal force of the main cable on the top of the tower during the lifting, the transportation and suspension of the arch rib section is \(F(t)\), and the horizontal displacement generated by the top of the tower is \(H(t)\). Assuming that the section of wind cable is section \(A\), the elastic modulus is \(E\), the resistance level of push-pull stiffness is \(K\), the horizontal tensile stiffness coefficient of wind cable is \(r_{i}\), then:  
\[H(t)=\frac{F(t)}{K+\sum ir_{i}A_{i}/L_{i}} \tag{10}\]  
As seen from the formula, the horizontal displacement of the tower is determined by the horizontal bending stiffness of the tower and the area of the wind cable, while the strength of the wind cable is far from being exerted.
# Key Factors of the Initiation and Development of Polygonal Wear in the Wheels of a High-Speed Train
## 5 Key Conditions of Initiation and Development of Polygonal Wear
### Effect of Different Diameters and Different Speeds  
The train is operated at different speeds \(v\) on different track lines, and the excited resonance frequency that influences the polygonal wear development does not vary considerably in the analysis during the field tests. Therefore, the operating speed determines the wavelength \(\lambda\) of the polygonal wear, and the integral multiple condition determines the initiation and high-rate development of polygonal wear. Therefore, the high-rate development of the polygonal wear depends on the operation speed and wheel diameter.  
The analysis of polygonal wear development during the entire normal use life of the wheel was conducted for the following case. The analysis involves different wheel diameters, different operation speeds, and excited resonance frequencies related to the polygonal wear development at a high rate. In this analysis, the diameter was from 820 to 920 mm, the speed was from 150 to 350 km/h, and the excited resonance frequency was 576 Hz, which was assumed to refer to the test results discussed above. For the other excited resonance frequencies, a similar analysis can be conducted. The calculated wear loss along the circumference of the wheel (50 rolling cycles) with different diameters and different speeds is shown in Figure 19. In the figure, the dark areas indicate severe polygonal wear or polygonal wear development at a high rate. Special diameters that would lead to severe polygonal wear are different if the operating speeds are different. At the same speed, there are several special diameters at which the polygonal wear develops fast. Therefore, with the help of numerical results, high-rate polygonal wear development can be predicted theoretically, and the re-profiling rule of polygonal wheels can be built up easily. For example, for a train at an operating speed of 350 km/h, there are two special diameters--860 and 915 mm--at which there are dark areas (Figure 19). Therefore, after the new wheels are placed into operation in the initial stage where the diameter is \(\sim\)915 mm, polygonal wear occurs; however, it is not considerably serious. The wheel diameter decreases to \(\sim\)860 mm because of wear and re-profiling, and because another dark area is generated. Thus, it implies that this is a time for high-rate polygonal wear development. The corresponding wheel diameter is the \"special diameter.\" This time, polygonal wheels need to be re-profiled in time, or the re-profiling interval should be changed, or the train operation speed is changed considerably to decrease the fast development of polygonal wear. For any new type of high-speed train to be placed into commercial operation, the easily excited resonant frequencies related to the initiation of the polygonal wear should be known, and then, figures similar to Figure 19 can be drawn and analysed.
## 2 Related Work  
The problem of optimizing WSN in multi-hop networks has been widely addressed in many research articles. Among them, different network metrics such as latency and lifetime are usually used as the main parameters for optimization criteria. Moreover, Mixed Integer Programming models cover the majority of the evaluations thanks to its precision when delivering results.  
For instance, in [11], critical node removal impact in network latency is analyzed by means of an MIP model inserted into a framework that iteratively removes critical nodes one after the other. Latency is calculated as the overall sum of the number of hops needed to transmit data from each node up to the base station. Since the problem is formulated and solved using a MIP model, it is time-consuming to calculate simultaneous removals. Due to this, authors only calculate the impact of the first and second most critical nodes in randomly generated networks with up to 100 sensors distributed in a disk shaped area of 200 m radius.  
Equally, The research in [12] analyzes the same impact by using similar input data and system model layout but focusing on network lifetime this time. Even though the MIP models are very similar, authors here go beyond the threshold of [11] by calculating the impact of up to five critical nodes in networks of 50 sensors with two different removal techniques: iteratively and simultaneously. In this case, lifetime is defined as the number of time slots needed until the first node runs out of battery life, taking into account that every node transmits one packet each time slot. Similarly, authors in [7] deeply study the case of eliminating the node that offers more network degradation regarding latency. Results show the evolution of network degradation as the radius of the network decreases as well as when the number of nodes varies.  
Another enhancement of the cited model is found in [13], where authors merge both metrics, latency and lifetime, and evaluate the network degradation in lifetime when limiting the overall number of hops, i.e., when limiting maximum latency. Studied network sizes vary from 30 to up to 90 sensors.  
Even though MIP models are very good at delivering exact results for many optimization criteria, they lack scalability. All the work cited beforehand share the same WSN structure: few network nodes and, overall, only the most and the two most critical nodes are studied.  
In order to extend such scenarios, heuristics offer a good opportunity for obtaining close to optimal results in larger situations both in terms of network density and number of critical nodes. In this work, we try to cover this literature space that is yet to offer relevant results by presenting a GRASP meta-heuristic for networks up to 1000 nodes in a 630 radius area. These values are not arbitrary but scaled up from the base scenario of 100 nodes and 200 network radius presented in the aforementioned literature.
## 6 Conclusions  
A novel multibeam antenna concept based on a bifocal dual-reflectarray configuration with an elliptical main reflectarray (3.55 m \(\times\) 1.81 m) has been proposed to generate complete multibeam coverage for transmission from a GEO satellite operating in the Ka-band. The bifocal technique has been applied for the first time to achieve a 50% reduction of the beam deviation factor with respect to the equivalent monofocal antenna. The proposed multibeam antenna requires a smaller main aperture (about half of the area) and shows larger radiation efficiency than other oversized antennas that have been proposed for the same application. An analogous design process can be performed to produce the beams at Rx, if appropriate reflectarray cells that will enable independent phasing at Tx and Rx frequencies are used. The proposed multibeam antenna will allow an important reduction in the volume and weight of the antenna system on board the satellite.  
Conceptualization, E.M.-d.-R., J.A.E. and G.T.; methodology, E.M.-d.-R. and J.A.E.; software, E.M.-d.-R.; validation, E.M.-d.-R. and J.A.E.; investigation, E.M.-d.-R. and J.A.E.; writing--original draft preparation, E.M.-d.-R.; writing--review and editing, J.A.E. and G.T.; visualization, E.M.-d.-R. and J.A.E.; supervision, J.A.E. and G.T.; project administration, J.A.E. and G.T.; funding acquisition, J.A.E. and G.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the European Regional Development Fund and the Spanish Ministry of Economy and Competitiveness, under Project TEC2016-75103-C2-1-R, and by the European Space Agency ESTEC, under Contract 4000117113/16/NL/AF.  
The authors declare no conflict of interest.
## 4 Experiments
### Noise Injection  
Adding (Gaussian) noise to input images did not, in general, result in improved generalization performance. We experimented with different amounts of added noise, Gaussian noise with standard deviation \(\sigma\in\{0.01,0.05,0.1,0.2\}\). The final model was trained with parameter \(\sigma\) that had the best performance on validation data. Examples of noise injected images from CIFAR-10 and Fashion-MNIST data are shown in Figure 10. Results are given in Table 7. Only in one case, we observe slight improvement in the test set accuracy, while accuracy on the training set remains close to 100%. Adding noise makes real classes harder to separate. Models have the capacity to learn available training data (training accuracy is in the worst case reduced to 99.74%), but the learned separation criterion also captures the injected noise. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \(L_{2}\) & \(L_{1}\) & **NAG** \\ \hline min & \(4.8\times 10^{-8}\) & 0 & \(5.57\times 10^{-8}\) \\ \(1 st\) quartile & \(7.49\times 10^{-5}\) & \(7.02\times 10^{-10}\) & \(1.34\times 10^{-4}\) \\ median & \(1.47\times 10^{-4}\) & \(2.12\times 10^{-9}\) & \(2.73\times 10^{-4}\) \\ \(3 rd\) quartile & \(2.22\times 10^{-4}\) & \(3.54\times 10^{-9}\) & \(2.74\times 10^{-4}\) \\ max & 3.5251 & 6.6516 & 2.3028 \\ \hline mean & 0.0216 & 0.0076 & 0.0349 \\ std & 0.0275 & 0.0426 & 0.0321 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summary statistics of _absolute_ values of weights. Figure 8: Loss learning curves of models that incorporate \(L_{2}\) and \(L_{1}\) weight decay in baseline models trained on Fashion-MNIST dataset. Figure 9: Comparison of baseline’s weights with weights obtained by models that use weight decay regularization methods with regularization parameter \(\lambda=5\times 10^{-5}\). Although the original paper [20] states that Batch Normalization fulfills some of the goals of Dropout and therefore removes the need for using the Dropout regularization method, from the results reported in Table 8 and the accuracy learning curves in Figures 13 and 14 (_Model 1_ and _Model 2_), we can see that the combination of these two techniques can benefit final performance of a given model. In all four cases, both validation and training accuracy increase compared to the only Dropout model. Batch Normalization inclusion into _Model 1_ and _Model 2_ also speeds up the learning process. However, in the case of Model 3 with the largest number of parameters, Dropout-Batch Norm combination indeed harms the model's final classification performance. In Figure 13, the validation accuracy learning curve of _Model 3_ significantly drops when we introduce Batch Normalization together with the Dropout.
## 2 Experimental Details
### Characterization Techniques  
X-ray diffraction (DIATOME-Pananalytical, Malvern, UK) of the CuCo\({}_{2}\)S\({}_{4}\)-deposited Ni-foams was measured with Cu K (1.54 A) radiation to dissect the crystalline phase formation. Cu, Co, and S elemental states were investigated using X-ray photoelectron spectroscopy (XPS; Versaprobe II, ULVAC-PHI Inc., Chigasaki, Kanagawa, Japan). The evolution of the morphological properties was assessed using a field-emission scanning electron microscope (FE-SEM; S-4800, Hitachi, Ibaraki, Japan). The energy-dispersive X-ray spectroscopy (EDS) and elemental mapping were assessed using the same FE-SEM. The inside view of the morphology was evaluated using transmission electron microscopy (TEM; JEOL/JEM-F20, Akishima, Tokyo, Japan).