esa-sceva/satcom-chunk-collection huggingface dataset:
# Intelligent Intrusion Detection of Grey Hole and Rushing Attacks in Self-Driving Vehicular Networks
## 3 The Proposed Intrusion Detection System
### Feature Sets and Extraction  
As indicated above, the IDS is based on a trace file which was extracted from ns-2. The IDS deduces from the features whether the behaviour of vehicles is normal or malicious. Overall, extracting features from a trace file for the purpose of an IDS could be difficult as it involves large and overlapping data. We made use of the AWK language to obtain features that define the actions of vehicles in VANETs [34]. They define five events: receive (r), send (s), drop (D), movement (m), and forward (f) [35].  
The detection system rate and false alarms numbers and the features were obtained from the trace file as follows:  
1. Generate the trace file from ns-2.
2. Use the AWK language to evaluate the output file from ns-2. The trace file is processed using the AWK language to determine the packet delivery rate (PDR), end-to-end delay and throughput.
3. Produce 21 features that define normal and malicious behaviour.
4. Choose 15 features that, based on our previous study [25], accurately reflect the behaviour of self-driving vehicles. Proportional Overlapping Score (POS) were used to extract significant features from the trace files.  
Table 2 shows the 15 selected features from the entire features space extracted from the trace file that are generated in ns-2.  
POS is considered the most suitable and efficient scheme with a dataset that has common classification problems such as outliers and high-dimensional binary [36; 37]. It was employed to calculate the overlapping rate in the features extracted. The R code the POS method using the following pseudocode [36]:  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Simulator & ns-2.35 RC7 \\ Simulation time & 499 s \\ Number of nodes & 40 Vehicles \\ Number of RSUs & 9 RSUs \\ Type of Traffic & Constant Bit Rate (CBR) \\ Topology & 600 \(\times\) 600 (m) \\ Transport Protocol & UDP \\ Packet Size & 512 \\ Routing Protocol & AODV \\ Channel type & Wireless \\ Queue Length & 50 packets \\ Number of Road Lanes & 2 \\ Radio Propagation Model & Two Ray Ground \\ MAC protocol & IEEE 802.11p \\ Speed & 30 m/s \\ Interface queue type & Priority Queue \\ Network Interface type & Physical Wireless \\ Mobility Models & Manhattan Mobility Model \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulator Environmental and Parameters.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Basic Trace** & **IP Trace** & **AODV Trace** \\ \hline Packet ID & & Packet Tagged \\ Payload Size and Type Source and Destination & & Hop Counts \\ Destination MAC Ethernet & IP Source and Destination & Broadcast ID \\ Destination MAC Ethernet & & Destination IP with Sequence number \\  & & Source IP with Sequence number \\ \hline \hline \end{tabular}
\end{table}
Table 2: Features Selection.  
All extracted features were tagged with the repetition value, and then the features with the lowest weight were removed.
###### Abstract  
The gap-induced plasmonic response of metallic nanoparticles drastically changes the near and far-field properties of nanoparticle antenna. Similar to a pair of metallic nanostructures, the two nanoparticles, with a dielectric core and silver shell in close proximity, exhibit multiple high energy plasmonic resonances at the short wavelength end of their optical spectrum. In this article, we have overwhelmingly investigated the disparity in the electric field of a core-shell dimer antenna when the gap between nanoparticles within the dimer becomes sub-nanometer in length. We used an electromagnetic planewave to excite the core-shell nanoparticles within the dimer. Frequency domain Finite Element Method (FEM) was employed for the numerical optical analysis of a dimer comprised of two silver-coated silica (SCS) nanoparticles in close proximity, using Computer Simulation Technology (CST) Microwave Studio. A modified Drude model has been used to predict the optical properties of the system with incorporating the size effects. The SCS dimer was numerically analyzed in the visible frequency band, and anomalies in near-field plasmonic coupling were investigated in detail. The inter-surface gap \(g\) between nanoparticles within the dimer varied in a range from 0.1 to 402 nm.  
Keywords:core-shell nanoparticles; silver; coupling; near field; Multiple dipolar modes; gap; Plasmons +
Footnote †: journal: Applied Physics Letters B
## 2 Materials and Methods  
### LMD and Its Improved Algorithm  
Based on LMD, the vibration signal of rolling bearing is decomposed into a series of PFs by cyclic iteration. The algorithm includes a large outer loop and a small inner loop. The inner small loop is assigned to extract the envelope signal and a purely frequency modulated signal which can combine into PFs. The large outer loop is designed to extract PFs from the vibration signal. The specific steps of LMD are described in detail in [5]. After decomposed by LMD, the raw signal \(x(t)\) can be reconstructed according to  
\[x(t)=\sum_{i=1}^{m}PF_{i}(t)+u_{m}(t) \tag{1}\]  
where \(PF_{i}(t)\) is PFs, \(u_{m}(t)\) is the residual signal, and \(m\) denotes the number of PFs.  
LMD also has its own defects, such as sliding step selection, mode mixing, sifting stopping criterion, and end effect. It is essential to improve the algorithm of LMD to make the decomposition convergence, achieve a good decomposition effect, and extract fault feature information more accurate. Some scholars have put forward different methods to solve the problem of sliding step size, such as one third of the longest local mean [5] and cubic spline interpolation method [30]. Other scholars proposed the method of extending the signal on both sides to solve the problem of end effect [31]. Ensemble local mean decomposition successfully overcomes the mode mixing and can efficiently eliminate various interference contents while preserving fault characteristic information [32]. Related parameters of these methods need to set manually and maybe they are not the best one. Therefore, these methods are not self-adaptive. Self-adaptive sifting stopping criterion builds an objective function, which can determine whether the screening result converges or not, and stop the sifting to ensure the best effect of PFs in the case of the closest theoretical stopping criterion [33]. The algorithm of LMD and self-adaptive sifting stopping criterion are detailed in References [5; 33]. The improved algorithm of LMD [33] is used to decompose the vibration signal in this paper.
## 5 Results  
### Experimental Setup  
The experiment was conducted on a Windows 10 operating system, utilising Python 3.8 as the primary programming language. To construct and train the TCN-CBAM model, the PyTorch 13.1 deep learning framework was chosen. Additionally, NumPy 2.0 was selected for efficient numerical computations, Pandas 2.0.0 for data manipulation and analysis, Matplotlib 3.4.3 for creating visualisations, and Scikit-learn 0.24.2 for implementing machine learning algorithms. For ease of development and debugging, the versatile and user-friendly Jupyter Notebook served as our main development environment.  
The hardware setup for the experiment boasted a high-performance Intel Core i9 processor (Intel, Santa Clara, CA, USA), ensuring swift responsiveness when handling substantial data volumes. Furthermore, to expedite the training and inference processes of the model, an NVIDIA GeForce 4070 Ti graphics card (Nvidia, Santa Clara, CA, USA), equipped with adequate CUDA cores and memory, was utilised.  
The TCN-CBAM model developed in this study comprises an input layer, four stacked dilated causal convolutional residual blocks, a CBAM attention module, two fully connected layers, and an output layer, as illustrated in the accompanying diagram. The essential hyperparameters for each component are detailed in Table 1.  
During the training process, the Adam optimizer was selected for updating the model parameters. Adam is an optimization algorithm that features adaptive learning rates. It  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Function Block** & **Hyperparameter** \\ \hline Input layer & size: 3 \(\times\) 1000 \\ \hline \multirow{4}{*}{Dilated causal convolution residual block} & \multirow{4}{*}{Normalization layer Activation layer Dropout layer} & Kernel size: 1 \(\times\) 3 \\  & & Number of kernels: 64 \\  & & Dilation factor: 2i \\  & & Output dimensions: 128 \\  & & Input dimensions: 128 \\  & & Output dimensions: 1 \\ \hline Output Layer & & Output size: \(2\times 1\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of model test results’ accuracy.  
adjusts the learning rates of the model parameters by computing the first-order moments (mean) and the second-order moments (uncentered variance) of the gradients. In each iteration, [PERSON] employs gradient information alongside historically accumulated variables to revise the momentum and the adaptive learning rate of the parameters, incorporating bias correction. Ultimately, the corrected momentum and adaptive learning rate are used to determine the parameter updates, thereby optimizing the model's weights and biases. During the experimentation, an initial learning rate of 0.0001 was set for the model, with a training batch size of 32 and a total of 1500 iterations.
# Trajectory Planning and Optimization for a Par4 Parallel Robot Based on Energy Consumption
## 4 Trajectory Optimization Based on GWO
### Trajectory Optimization
#### 4.2.1 Mechanical Energy Consumption of the Par4 Parallel Robot  
For the Par4 parallel robot, the total power of its motors is the sum of the circuit thermal power, the magnetic field energy storage power and the mechanical power, among which the circuit thermal power and magnetic field energy power are relatively small, and consequently, the total energy consumption of the robot can be considered equal to the mechanical energy consumption. The instantaneous mechanical power of the Par4 robot is calculated as follows:  
\[p(t)=p_{1}(t)+p_{2}(t)+p_{3}(t)+p_{4}(t) \tag{35}\]  
where \(p_{i}(t)(i=1,2,3,4)\) is the instantaneous mechanical power of each drive unit, and \(t\) is the time, in seconds.  
Assuming that only energy consumption braking is applied, the instantaneous mechanical power of each motor in the Par4 parallel robot is:  
\[p_{i}(t)=\left\{\begin{array}{cc}\tau_{i}(t)\hat{q}_{i}(t),&\tau_{i}(t)\hat{ q}_{i}(t)>0\\ 0,&\tau_{i}(t)\hat{q}_{i}(t)\leq 0\end{array}\right. \tag{36}\]  
Then, the total mechanical energy consumption of the Par4 parallel robot can be calculated as follows:  
\[E_{m}=\int_{0}^{T}p(t)dt=\int_{0}^{T}\sum_{i=1}^{4}p_{i}(t)dt \tag{37}\]  
where \(E_{m}\) is the total mechanical energy consumption, and T is the running period in seconds.
# MIMO LS-SVR-Based Multi-Point Vibration Response Prediction in the Frequency Domain
## 3 Experimental Verification
### Parameter Settings of the MIMO LS-SVR Regression Prediction Model  
For each frequency, a MIMO LS-SVR prediction model must be built. Additionally, in this experiment, the frequency value was between 0 Hz and 6400 Hz, and the frequency resolution \(\Delta f\) was 4 Hz. Hence, the total number of different frequencies \(\Omega\) was 1601. Thus, 1601 MIMO LS-SVR models had to be built.  
The MIMO LS-SVR uses an RBF kernel function. Additionally, to obtain the best value of the hyperparameters (for LS-SVR, they were \(\sigma\) and \(\gamma\)), a grid search method was applied to obtain the most suitable parameter values, in which we first tested several combination values, then set a range around the best combination value, and then grid searched within the range. The parameters of the penalty and regularization terms were optimally solved using a grid search, and the obtained gamma was 20.0 and sigma was 49.5. The experimental results of the vibration response prediction based on MIMO LS-SVR were obtained for the above parameters.
# Investigation of Titanium Alloy Cutting Dynamics in Thin-Layer Machining
### Research Objectives and Approach  
The aim of this study is to investigate the minimum undeformed chip thickness \(h_{min}\) and its relationship with cutting dynamics during the micro-machining of titanium Grade 2 and Grade 5 alloys.  
The main contribution of this paper is the experimental and analytical investigation of the minimum undeformed chip thickness in the micro-machining of commercially pure titanium Grade 2 and titanium alloy Grade 5 (Ti-6 Al-4V). By employing a measurement system capable of simultaneously recording cutting force components and temperature during machining, the study enables the identification and analysis of the transition point between ploughing and shearing. This is further supported by a modeling approach that describes the shift from elastic-plastic deformation to chip formation, offering a mechanistic understanding of material removal at the microscale.  
This study offers a more comprehensive perspective than many previous works by integrating nonlinear dynamics analysis, including the 0-1 test for chaos, to classify the nature of cutting force signals. This approach allows for the detection of dynamic instabilities and chaotic behaviors, which are often overlooked in conventional machining studies. Additionally, the simultaneous monitoring of cutting forces and temperature provides a more comprehensive understanding of the material removal process, particularly in machining titanium alloys known for their poor machinability.  
This paper addresses a relevant and technically challenging problem in precision manufacturing. The results not only advance the theoretical understanding of chip formation mechanisms under micro-machining conditions but also have clear practical implications. By offering insights into process optimization, this study supports improvements in surface finish, tool life, and overall machining reliability for titanium-based components used in high-performance industries.
# Telemedicine: A Survey of Telecommunication Technologies, Developments, and Challenges
## 5 Use Cases
### Emergency Telemedicine  
In [87], an emergency telemedicine helicopter service was suggested to provide medical data and supply for life-saving services using the IEEE 802.11, 802.15, and 802.16 wireless standards. The proposed telemedicine system relied on satellite communication, thanks to its ability to provide instant access to broadband services in distant locations where medical services are inadequate or non-existent. Emergency telemedicine devices  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Types** & **Cell Radius (Km)** & **Users** \\ \hline Femtocell & 0.01–0.1 & 1–30 \\ Picocell & 0.25–1 & 30–100 \\ Microcell & 0.2–2 & 100–2000 \\ Macrocell & 8–30 & More than 2000 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Types of Small Cells and their Coverage Radii [50].  
allow telediagnosis and teleconsultation through wireless communication networks such as GSM, GPRS, and satellite. The system collects and transmits vital biosignals and still images of the patient to a remote consultant during an emergency. Real-time transmission of data is strongly desirable for prompt response. Videoconferencing or a real-time two-way interactive TV system are often used. High-speed internet with high-capacity wireless transmission technologies such as LTE, 5G, and BLE, can be used with compatible devices. Emergency telemedicine for the resuscitation of new-borns was demonstrated in [120]. The Newborn Resuscitation Technology Program (NRTP) uses a wired telemedicine cart with a hardware CODEC, HD camera, and microphone-speaker arrangement for communication.
# A Hybrid MCDM Model for Live-Streamer Selection via the Fuzzy Delphi Method, AHP, and TOPSIS
###### Abstract  
The development of the Internet is a key revolution of the 20 th century. The Internet has led to a boom in e-commerce. Online shopping is generally the most popular shopping option for consumers. The coronavirus disease 2019 (COVID-19) pandemic has resulted in adopting various measures, such as lockdown and stay-at-home orders, in various countries. This has led to changes in people's consumption habits. In Taiwan, consumer behavior has also rapidly shifted to online shopping after the outbreak of the COVID-19. As livestreaming breaks time and space barriers, its real-time, interactive, and authentic features are unparalleled compared to other marketing methods, thereby creating a brand-new shopping experience for consumers. Accordingly, livestream shopping, a new consumption model, has developed and become an essential part of people's daily lives. Live-streamers, who are similar to salespersons in traditional markets, play a vital role in e-commerce livestreaming. The success of livestream shopping has highlighted the importance of live-streamers. The competition among live-streamers has become more intense because of the arrival of many newcomers. Thus, operators must make careful hiring decisions. However, no related literature in the past has investigated this important topic. Therefore, this study applied a hybrid multi-criteria decision-making (MCDM) model comprising the fuzzy Delphi method (screen the selection criteria), analytic hierarchy process (AHP) (obtain the weight of each dimension and criterion), and technique for order preference by similarity to ideal solution (TOPSIS) (rank the alternatives) to assist the managers of shopping websites in selecting live-streamers. We interviewed the managers of shopping websites and reviewed the related literature to compile the selection criteria. Following the fuzzy Delphi method, 15 important selection criteria were retained based on the 30 managers' opinions. Further, the criteria were classified into dimensions based on the previous literature and interviews conducted on managers to establish a hierarchical framework. On the basis of this hierarchical framework, AHP and TOPSIS were applied to help a case company select live-streamers. A comparative analysis between the outcomes from AHP and AHP/TOPSIS was also conducted in this study. This study is the first empirical study on live-streamers' selection and adds to the literature on livestreaming.  
live-streamer; fuzzy Delphi method; analytic hierarchy process (AHP); technique for order preference by similarity to ideal solution (TOPSIS); shopping website
## 3 Proposed EP-MAC Protocol Design
### Virtual Clustering and Synchronization  
Virtual clustering technique [1] is used for synchronization of frames in the EP-MAC protocol. When a node comes to life, it first listens to broadcasted SYNC message from its neighbors for a predetermined time. During this period if the node hears a schedule message from a neighbor, it configures its schedule as its own. And then the node starts broadcasting this schedule as its own schedule to its neighbors. If the node does not receive a schedule for the predefined period, it chooses a frame schedule and transmits a SYNC packet. When a node has its own schedule but it receives another schedule from another node, if the schedule is different, then the node will adopt both schedules. It is because if the node knows the schedule of a neighbor node which belongs to another virtual cluster, only then the communication is possible between the nodes. During this phase, each node creates the one hop neighbor list of it and with later by using these one hop neighbors list the node constitutes the two hop neighbor list of it. After creations of the neighbor list each node is given an id and within a two hop neighbor this id will be unique.  
Each node will maintain a table which will record the possibility of the parallel transmission link within the two hop neighbors. The value of the required power for transmission within a particular pair of nodes will also be recorded. Both with transmission power adjustment and without transmission power adjustment will be recorded. Because, using the power adjustment features multiple transmissions can be done in one TDMA data slot. Even though the power adjustment features are not used more than one transmission is possible in some cases.
## Abbreviations  
\begin{tabular}{l l} \(d_{k}\) & diameter of damping diaphragms \\ \(d_{p}\) & diameter of throttling diaphragms of elastic orifices \\ \(H,h,h_{0}\) & dimensionless thickness, current thickness of the gap and its thickness for \(\varepsilon=0\) \\ \(K\) & dimensionless compliance of bearing \\ \(K_{\varepsilon}\) & dimensionless compliance of the suspension 6 \\ \(l,L\) & half of the length and dimensionless length of bearing \\ \(l_{1},L_{1}\) & half of the length and dimensionless length of the inter-row part \\ \(l_{2},L_{2}\) & length and dimensionless length of end parts \\ _mas, Mas_ & mass and dimensionless mass of the shaft \\ \(P(Z,\varphi,\tau)\) & dimensionless dynamic pressure in the carrier gap \\ \(P_{0}(Z)\) & dimensionless static pressure in the carrier gap for \(\varepsilon=0\) \\ \(p_{a}\) & ambient pressure \\ \(p_{k}\) & pressure of air on damping diaphragms \\ \(p_{p}\) & pressure of air in inter-throttle chambers \\ \(p_{s}\) & supply pressure \\ \(Q_{h}\) & dimensionless flow rate through the gap \\ \(Q_{k}\) & dimensionless flow rate through damping diaphragms \\ \(Q_{p}\) & dimensionless flow rate through elastic orifices \\ \(Q_{w}\) & dimensionless flow rate associated with compressibility of air in the inter-throttle chambers \\ \(r_{0}\) & the shaft radius \\ \(s\) & the Laplace transform variable \\ \(t_{0}\) & the scale of current time \\ \(v_{p_{r}},V_{p}\) & volume of inter-throttle chambers and their dimensionless volume \\ \(W\) & dimensionless bearing capacity \\ \(Z\) & dimensionless longitudinal coordinate \\ \(\varepsilon\) & dimensionless eccentricity \\ \(\varphi\) & dimensionless circumferential coordinate \\ \(\tau\) & dimensionless current time \\ \(\mu\) & coefficient of dynamic viscosity of the air \\ \(\eta\) & degree of stability \\ \(\underline{\varepsilon}\) & criterion of oscillations damping over a period \\ \(\sigma\) & the \"squeezing number\" \\ \(\chi\) & normalized adjustment coefficient of the external throttling system \\ \(\underline{\varsigma}\) & normalized adjustment coefficient of annular damping diaphragms \\ \(\Pi(P_{1},P_{2})\) & the Prandtl function \\ \end{tabular}