esa-sceva/satcom-chunk-collection huggingface dataset:
# Intrusion Detection for in-Vehicle Communication Networks: An Unsupervised Kohonen SOM Approach
## 5 Materials and Methods  
We tested a distance-based intrusion detection system to identify attack and anomaly messages injected on a CAN bus. The intrusion detection system was based on a hybrid unsupervised Kohonen SOM neural network in order to improve the efficiency of the model in detecting attack messages. We proposed a novel distance-based procedure to integrate the unsupervised Kohonen SOM network with the k-means algorithm and compared it with the traditional procedure. Both the methods were tested on open source data containing four datasets, each with a different type of attack: _DoS_ attack, _spoofing the drive gear_, _spoofing the RPM gauge_, and _fuzzy_ attack. The datasets were created by logging the CAN network 2.0B traffic via the OBD-II port from a real vehicle while message injection attacks were being performed [40; 52]. Each dataset contains a total of 30 to 40 minutes of CAN traffic with 300 intrusions of messages injected for 3 to 5 seconds. The Kohonen SOM network was first tested on single datasets separately; then, they were merged into a unique _mixed_ dataset containing all types of attack.  
In the _DoS_ attack database, attack messages with dominant CAN IDs are injected on the CAN bus with the aim of tampering with the accessibility to the network. The _spoofing gear_ and spoofing _RPM_ datasets contain attack messages concerning, respectively, the driver gear and the RPM gauge aimed at changing the status on the instrument panel. In the _fuzzy_ dataset, messages of spoofed random CAN ID and data values are injected in order to damage the vehicle's functionality, due to the manipulation of normal CAN ID and data values.  
The experimentation was carried out in the following steps (Figure 2):  
1. Data pre-processing: Data were pre-processed in order to be given input to the network. We analyzed open source car hacking data including different kinds of attack messages. In particular, we considered four different datasets containing _DoS_ attacks, _spoofing the drive gear_, _spoofing the RPM gauge_, and _fuzzy_ attacks. Data are available at [53].
2. Experimental process: We tested the proposed method based on a novel procedure to integrate the Kohonen SOM network with a k-means algorithm in order to improve the performance of the model in terms of accuracy in detection of attack messages and reduction of false negative rate. We also compared the proposed method with the traditional procedure. Both methods were tested, first, on each attack dataset separately, and then on the _mixed_ dataset.
3. Evaluation of the performances of the models.
## 6 Functional Properties and Applications of Nanocomposites
### Polymer-nanocomposite-photoresists  
In microelectronics and microsystems technology lithography, using suitable photosensitive polymer based resists is one of the most important techniques for the realization of nano- and microstructured layers [185, 186]. Two principal reaction mechanisms determine the selection of suitable polymer resists. In case of a positive-tone photoresist, the irradiation of a polymer film through a mask causes a photodegradation of the irradiated areas and a pronounced solubility of the material. The unexposed resist remains insoluble, a positive image of the mask structure appear after solvent development. In case of a negative-tone photoresist, the uncured resist polymerizes under irradiation forming a negative image of the mask structure. Typical representatives are PMMA for a positive resist and epoxides (e.g., SU8, Microchem. Corp.) for a negative resist. The addition of nanosized ceramic fillers to a photoresist is driven by the following aspired aspects:  
* improved sensitivity to electromagnetic radiation of a certain wavelength region
* improved resolution
* improved chemical resist stability
* improved mechanical stability during processing
* tailoring of the coefficient of thermal expansion
* introduction of new functionalities like electrical conductivity or magnetic properties
* direct fabrication of microstructured ceramic or metal components via microstereolithography (rapid prototyping). As an example, the addition of nanosized SiO\({}_{2}\) improved significantly the accessible resolution of standard resists for electron beam lithography from 131 nm down to 47 nm [243]. Surface modified nanosized SiO\({}_{2}\) (10-50 nm particle size) helped to improve the process stability of a photosensitive fluorinated polyimide by a TG and decomposition temperature increase by reduction of the polymer chain mobility due to the enhanced interaction of the particle large surface area and the polymer matrix [244]. Furthermore, the coefficient of thermal expansion was reduced by the filler. The new composite allowed for a direct fabrication of high temperature stable optical devices by UV-lithography. The addition of alumina with an average particle size of 400 nm to SU8 up to a solid load of 42 vol % enabled the direct fabrication of high aspect ratio ceramic microcomponents by deep x-ray lithography and thermal post-processing [245]. The same resist, modified with highly agglomerated nanosized silver particles (load up to 40 vol %), was used for the development of an electrically conductive resist, which can be patterned by the UV-lithography process [246]. The percolation threshold for conductivity was measured to be around a low silver content around 6 vol %. For further reading a comprehensive overview about polymer-filled composites applied as resist systems can be found in [247].
## 2 Background and Motivation
### Model Partitition  
A DNN model consists of many network layers. After implementing model partitioning on the original DNN, the original model can be split into multiple sub-models, representing different parts of the original model and maintaining the original weight information. As shown in Figure 1, we give a DNN sample that consists of five network layers, and the two red dashed lines have split it into three new sub-models: we named the three new sub-models \(sm_{1}\), \(sm_{2}\), and \(sm_{3}\). The \(sm_{1}\) sub-model is near the input end, and \(sm_{3}\) is near the output end. We give the same input data to the original model and \(sm_{1}\). For the original model, it would generate an output directly. For \(sm_{1}\), it would give the output feature to \(sm_{2}\) as input data. Next, the output of \(sm_{2}\) will also be used by \(sm_{3}\) as input data. Since the new sub-models still maintain the same weight as the original model, the output result of \(sm_{3}\) will be no different from the output of the original model.  
So, the model partition would not change the inference result if we retained the correct data transmission flow.
# Light-to-Plasma Momentum Transfer
## 4 Discussion  
The acceleration of the electrons parallel to \(\mathbf{k}\) in a plasma has often been referred to as \(\mathbf{J}\times\mathbf{B}\) acceleration in the laser-plasma literature, as it is caused by the electron current  
Figure 2: The electron kinetic energy per photon directed in the \(\mathbf{k}\)-direction in a plasma as a function of the square of the reduced vector potential \(a_{o}^{2}\) of incident light. The kinetic energy per photon is plotted in a reduced form \(n_{e}<U_{k}>/(\hbar\omega)/(\omega_{p}^{2}/\omega^{2})\), where \(\omega_{p}\) is the plasma frequency and \(\omega\) is the frequency of the light.  
flow **J**, parallel to the electric field producing a force due to the magnetic field **B** of the electromagnetic wave. For a pulse of light incident into a plasma, **J**\(\times\)**B** acceleration can be equivalently expressed as being caused by the ponderomotive force on each electron, which is represented by the gradients \(-\
abla<U>\) in the **k**-direction of the time-averaged ponderomotive energy \(<U>\) (see Section 4.1 Tallents [28]). The leading edge of a light pulse incident into a plasma with increasing ponderomotive energy accelerates electrons in the direction of the light propagation, while the trailing edge with decreasing ponderomotive energy de-accelerates the electrons back to a zero velocity. When a light pulse interacts with a low-density plasma in front of a solid target, the electrons can be accelerated to collide with the high-density plasma before being de-accelerated (resulting in the term **J**\(\times\)**B** \"heating\" of the target used in the laser-plasma literature; for example, in [2; 38]).  
Recently measured and simulated electron temperatures at laser irradiances from \(\lambda\lambda^{2}=10^{19}\) to \(10^{22}\) W cm\({}^{-2}\)\(\upmu\)m\({}^{2}\) have shown evidence that the focal spot size can affect the temperatures in laser-plasmas produced at high, constant irradiances [39]. We showed that the electron kinetic energy per photon for the electron motion in the direction of a beam, as shown in Figure 2, exhibites a maximum at a laser irradiance corresponding to the reduced vector potential \(a_{o}\approx 3.7\). For fixed laser pulse energy (and, hence, a fixed photon number), this result suggests that to ensure the maximum energy transfer from photons to electron motion, parallel to the **k**-direction, the laser focus should be adjusted to achieve \(a_{o}\approx 3.7\) (reduced irradiance \(\lambda\lambda^{2}\approx 2\times 10^{19}\) W cm\({}^{-2}\upmu\)m\({}^{2}\)), even if a tighter focus, with consequent higher \(a_{o}\) values, is possible.
## 1 Introduction  
As urbanization accelerates, urban rail transit networks face increasing imbalances in passenger flows, particularly during peak periods. Current strategies to enhance train headway reduction are often costly and lead to under-utilization during off-peak hours. A more efficient approach integrates both full-length and short-turn routes with variable train formations to align capacity with demand, reducing wait times and maintaining service regularity.  
However, implementing variable train formations demands substantial modifications to signaling systems, yard facilities, and equipment conditions, which may result in extended adjustment periods and temporarily reduced line capacities. Adopting virtual coupling addresses these challenges by enabling real-time, train-to-train wireless communications. This system shares operational data such as speed, position, and status, allowing dynamic, site-independent train coupling and uncoupling based on passenger demand. This method enhances operational efficiency and mitigates issues like wear on mechanical parts and compatibility with different train couplings.  
This study focuses on the integrated optimization of train diagrams and rolling stock circulation in urban rail transit, incorporating both full-length and short-turn routes for trains utilizing virtual coupling technology. By considering time-varying passenger flow demand as the input, we dynamically simulate the passenger boarding and alighting process. This simulation aims to optimize the scheduling of trains to match the dynamically varying passenger flows [1]. We introduce a virtual coupling decision variable in the model, allowing consecutive trains to determine whether to engage in virtual coupling at stations.  
The rest of this paper is organized as follows: Section 2 presents a literature review; Section 3 describes the establishment of the mathematical model and the design of solution algorithm; Section 4 discusses a case analysis; Conclusions are presented in Section 5.
## 7 Simulation Results and Discussions
### Transient Stability Analysis
#### 7.1.1 Analysis Using the Conventional Rotor-Side Controller  
Two cases are considered using the conventional rotor-side controller. The parameters for conventional PI controllers are chosen based on the method presented in the literature [23]. The power rating of each wind generator in Case 01 is DFIG = 59 MW and SCIG = 41 MW (total: 100 MW), and, in Case 02, DFIG = 58 MW and SCIG = 42 MW (total: 100 MW). Different power ratings of the wind generators are chosen, because the objective is to stabilize the maximum possible rating of SCIG by using lowest possible rating of DFIG, while the total capacity of WF is kept constant at 100 MW. In this present study, it is calculated by running the simulation for multiple times with different combinations of power ratings of the wind generators.  
Figure 17a,b show the responses of reactive powers, which indicates that the DFIG can provide the necessary reactive power during the severe symmetrical 3 LG fault in Case 01. As a result, the connection point voltage recovers to the rated value quickly in Case 01, as shown in Figure 18a. However, in Case 02, the DFIG does not provide the necessary reactive power during the fault condition. Thus, the connection point voltage cannot recover to the rated value. Since the connection point voltage does not satisfy the standard grid code of Figure 16 in Case 02, the WF is disconnected from the power system by opening CBs near bus 12 at 2 s. The rotor speed responses of both wind generators are stable in Case 01, but unstable in Case 02, as shown in Figure 19.  
Figure 17: Reactive power output of wind generators: (**a**) DFIG; (**b**) SCIG.  
Figure 18: Individual response of the WF at bus 12: (**a**) Voltage at connection point; (**b**) total active and reactive power at connection point.  
Figure 20 shows the active power output of DFIG and SCIG, respectively. The active power can recover to the nominal value in Case 01 for both wind generators, but failed to recover to the nominal value in Case 02. Moreover, the DC-link voltage of the DFIG becomes more stable in Case 01, as compared to Case 02, as shown in Figure 21a.  
Figure 22a,b show the active power output and rotor speed responses, respectively, of the conventional power plants (SGs). The active power and rotational speed of the SGs can return to the initial condition in Case 01. However, the active power of the SGs in Case 02 increases significantly after the WF has been disconnected, resulting in a rotor speed drop of the SGs. It is clear that the system becomes unstable in Case 02, which can also be seen from Figure 23, where the system frequency collapses in Case 02 after the WF has been disconnected.  
Therefore, the lowest power rating of the DFIG with the conventional rotor-side controller is 59 MW in order to stabilize the 41 MW SCIG. The DFIG can also stabilize the SGs.  
Figure 19: Rotor speed response of wind generators: (**a**) DFIG; (**b**) SCIG.  
Figure 20: Active power output of wind generators: (**a**) DFIG; (**b**) SCIG.
## 3 Sensor Network Architectures for Monitoring Underwater Pipelines
### RF Wireless Sensor Networks  
Another architecture that can be used for monitoring underwater pipelines is wireless sensor networks that use radio frequency. The main differences between this architecture and other wireless sensor networks used for above ground applications is that each sensor node is connected to a surface buoy through a cable as shown in Figure 5. Radio transceivers are available on the surface buoys. As a result, the nodes can communicate through radio frequency channels which provide better communication bandwidth, propagation delay, bit error rate and connectivity, as well as less power consumption for processing communication signals compared to the underwater acoustic sensor network. In addition, surface buoys can be equipped with solar cells to provide energy for the sensors and communication devices.  
Although this architecture has many advantages over the acoustic sensor network, it has two major physical security problems. First, the floating buoys and their cables may be damaged by passing ships. In addition, the locations of the floating buoys are a good indication for the location of the pipeline. Therefore, the pipeline can be easily discovered and sabotag  
Figure 4: Underwater acoustic sensor network.  
connected through wires to an underwater pipeline in very deep seas is not a practically feasible solution. These wires will be vertically expanded and as the length increases to the sea surface, the probability that the wires will be damaged (accidentally or due to sea currents) also increases.
# Public Key-Based Authentication and En-Route Filtering Scheme in Wireless Sensor Networks
## 6 Performance Evaluation  
The authors evaluate the performance of PDF [19], DSEDA [1], and PKAEF through simulation experiments based on the WSN simulator in the literature [12], which was developed in C++ language on a PC with dual-core 1.6 GHz CPU and 8 GB memory. The authors simulated a network with two event areas, and randomly deployed 10 sensor nodes in each event area (\(Nc=10\)). According to the distance from the center of the event area to the BS, two different scenarios were simulated. In Scenario 1, the distance from the center of the event area to the BS was 300 m, and 20 forwarding nodes were deployed between the BS and each event area. In Scenario 2, the distance from the center of the event area to the BS was 600 m, and 40 forwarding nodes were deployed between the BS and each event area. The other simulation parameters are provided in Table 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Overhead** & **PKAEF** & **PDF** & **DSEDA** \\ \hline Communication overhead for report generation & ** & ** & ** \\ Communication overhead for forwarding a report & * & * & ** \\ Key storage overhead & *** & **/******* & * \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of overhead.  
In the simulation, the SECG recommended 160-bit elliptic curve (secp160r1) was selected and the 32-bit RC5 algorithm was used as the symmetric-key encryption algorithm. The evaluation focuses on the following aspects: energy expenditure for report generation; performance of false report filtering; resilience to report disruption attacks; resilience to selective forwarding attacks. All the experiment results were averaged over 1000 runs.
# Low Ionosphere under Influence of Strong Solar Radiation: Diagnostics and Modeling
## 2 Methods
### Simulated VLF Signals  
The LWPC program package [30] can be used for simulation of VLF propagation along any particular great circle path (GCP) under various diurnal, seasonal, and solar cycle variations in the ionosphere. This program package commonly executes the calculations for all possible modes and was tested with experimental data. In the published papers, there are few models for simulation of VLF propagation in the Earth-ionosphere waveguide (EIWG), such as the FDTD method, modefinder, etc. [31; 32]. These models have been applied in many studies for calculations of various ionospheric parameters when characteristics and the ionospheric state allow applying some approximations.  
In this contribution, by applying the LWPC code, the propagation of VLF signal was simulated for normal ionospheric condition, with the aim to estimate the best fitting pairs of [PERSON]'s parameters \(H^{\prime}\)nor and \(\beta_{\rm nor}\) (reflection height and slope of sharpness, respectively; _nor_ denote normal ionosphere condition) to obtain values of VLF amplitude and phase as close as possible to the recorded data for a selected day [33].  
The next step was to simulate propagation of VLF signal through EIWG in the perturbed lower ionosphere during the flare event induced by an increased X-ray flux. For this step, we needed monitored, i.e., observed, VLF data to examine the signal perturbations during the solar X-ray flare.  
We selected the pair of \(H^{\prime}\) and \(\beta\) and used it as input parameter in the LWPC code to obtain the VLF signals, i.e., the simulated data of amplitude and phase at the receiver site. The process was rerun until differences between the simulated signal (\(\Delta A_{\rm sim}\) and \(\Delta P_{\rm sim}\)) for the disturbed and normal condition matched the measured ones \(\Delta A\) and \(\Delta P\). After accomplishing a fair matching: \(\Delta A_{\rm sim}\approx\Delta A\) and \(\Delta P_{\rm sim}\approx\Delta P\), the corresponding pair of \(H^{\prime}\)per and \(\beta_{\rm per}\) was finally obtained (_sim_ means simulated, _per_ denote perturbed condition). The corresponding pair of \(H^{\prime}\)per and \(\beta_{\rm per}\) was used for calculation of the electron density at a function of height for moments during the solar flare event (see schematic presentation in Figure S2).  
Electron densities were resolved from the observed VLF amplitude and phase perturbations by a trial-and-error method where density profile was adjusted until the simulated amplitude and phase paired with observed disturbed data, as explained in the above paragraph. In this way, by processing data, the acquired Wait's parameters \(H^{\prime}\)per and \(\beta_{\rm per}\) were used for our further calculations.
# Reliability Assessment of Cyber-Physical Distribution Systems Considering Cyber Disturbances
## 3 Distribution Network CPS Reliability Modeling  
### Fault Tree Analysis Model  
Fault trees are logic diagrams that describe event causality by event symbols, transfer symbols, and logic gate symbols [34]. The fault tree analysis is an analysis method that analyzes the causes of faults in each component of the distribution network CPS to determine all combinations of the fault causes and the probability of occurrence [35]. The analysis steps of the fault tree are shown in Figure 4.  
The procedure of constructing the fault tree in this paper includes the following: first, determining the top event, which is the most undesired situation of the system; then analyzing all possible causes of the top event, analyzing the logical relationship between the causes, and connecting them with logic gates; and then analyzing whether the input events directly connected with the top event can be further decomposed, and if so, treating them as the input events of the next level of events and decomposing them until all the input events are no longer decomposable, and the events that cannot be decomposed are the bottom events.  
A typical distribution system is shown in Figure 5. The distribution system is a typical radial distribution system consisting of two feeders, with three main lines L1, L2, and L3, and three branch lines L4, L5, and L6. The main lines are separated from each other by isolation switches, and T is the contact switch.  
Taking the top event of Load B failure as an example, the possible causes of the top event include the failure of main line L2, failure of branch line L5, and disconnection of the isolation switch. Since the failure of the main line L1 and main line L3 is the cause of the isolation switch disconnection, and as long as one of all possible causes occurs, Load B is bound to fail. Therefore, without considering the failure of the circuit breaker and contact switch inputs, the fault tree of Load B is shown in Figure 6.  
Figure 4: Block diagram of the reliability assessment method.  
Figure 5: Typical power distribution system structure.  
Figure 6: Fault tree of Load B failure.  
In Figure 6, L2 and L5 represent the failure of the main line L2 and the branch line L5, respectively; L1 and L3 represent the disconnection of the isolation switch due to the main lines L1 and L3, respectively. All the possible causes of Load B failure in the typical radial distribution system of Figure 5 are clearly represented in Figure 6. The analysis of the fault tree allows the Petri net model of the system to be obtained, which in turn enables a series of reliability indices to be solved.