esa-sceva/satcom-chunk-collection huggingface dataset:
# On Modeling Eavesdropping Attacks in Underwater Acoustic Sensor Networks +
## 1 Introduction
### Major Contributions  
Generally, in conventional UASNs, each node is equipped with an _isotropic hydrophone_, which collects acoustic signals in all directions. We call such underwater acoustic sensor networks with isotropic hydrophones as _IUSNs_. Compared with an isotropic hydrophone, an _array hydrophone_ can collect acoustic signals on desired direction. In other undesired directions, there are no signal or weakened signal. Thus, using array hydrophones in UASNs can potentially reduce the interference and consequently improve the network performance. For example, it is shown in [36] that using array hydrophones in UASNs can significantly improve the network throughput. We call such underwater acoustic sensor networks with array hydrophones _AUSNs_. In this paper, we investigate the eavesdropping activities in both IUSNs and AUSNs. We also consider two cases in which an eavesdropper is equipped with an isotropic hydrophone and an array hydrophone, respectively. We name an eavesdropper with an isotropic hydrophone as an isotropic eavesdropper and name an eavesdropper with an array hydrophone as an array eavesdropper.  
Our major research contributions in this paper can be summarized as follows.
* We formally propose an analytical model to investigate the probability of eavesdropping attacks in both IUSNs and AUSNs with consideration of underwater acoustic channel conditions, including signal attenuation and ambient noise. In particular, we establish the relationship between the _eavesdropping success condition_ and the underwater acoustic signal channel. We further derive the eavesdropping probability with consideration both isotropic eavesdropper and array eavesdropper, respectively.
* We conduct extensive simulations to validate the effectiveness and the accuracy of our proposed model. The simulation results match the analytical results, indicating that our proposed model is accurate.
* We compare the eavesdropping probability of IUSNs and AUSNs. In particular, we find that the eavesdropping probability of AUSNs is lower than that of IUSNs, implying that using array hydrophones in UASNs can reduce the eavesdropping probability. We also find that an array eavesdropper has a higher eavesdropping probability than an isotropic eavesdropper in both IUSNs and AUSNs.
* We find that the eavesdropping probability heavily depends on the acoustic signal frequency, spreading factor, wind speed and the node density. Our results pave the way for designing a better protection mechanism in UASNs.  
The rest of this paper is organized as follows. Section 2 first presents the channel model in underwater acoustic communications. We then introduce the transducer used in this paper in Section 3. We next analyze the eavesdropping attacks in UASNs in Section 4. Section 5 gives the empirical results with comparison of IUSNs and AUSNs considering two kinds of eavesdroppers. Finally, we conclude the paper in Section 6.
# Multi-Objective Motion Control Optimization for the Bridge Crane System
###### Abstract  
A novel control algorithm combining the linear quadratic regulator (LQR) control and trajectory planning (TP) is proposed for the control of an underactuated crane system, targeting position adjustment and swing suppression. The TP is employed to control the swing angle within certain constraints, and the LQR is applied to achieve anti-disturbance. In order to improve the accuracy of the position control, a differential-integral control loop is applied. The weighted LQR matrices representing priorities of the state variables for the bridge crane motion are searched by the multi-objective genetic algorithm (MOGA). The stability proof is provided in order to validate the effectiveness of the proposed algorithm. Numerous simulation and experimental validations justify the feasibility of the proposed method.  
Keywords:anti-disturbance; bridge crane system; linear quadratic regulator (LQR); multi-objective genetic optimization (MOGA); trajectory planning +
Footnote †: journal: Applied Sciences
## 8 Model Application Analysis
### Clinical Decision Support and Training Tools  
Beyond direct intervention applications, our model and its visualization capabilities can serve as valuable tools for clinical decision support and professional training in clinical settings. The Grad-CAM visualization technique applied to our model reveals which facial regions and speech features are most indicative of different emotional states in children with autism. This information can be leveraged to develop clinical assessment aids that helpprofessionals objectively evaluate emotional expressions during diagnostic and therapeutic sessions, potentially increasing the reliability and consistency of clinical judgments across different practitioners.  
These visualizations could be incorporated into professional training programs that teach therapists, educators, and parents to recognize subtle emotional cues in children with autism. By highlighting the specific facial regions and speech characteristics that distinguish different emotional states, our system could accelerate the development of expertise that typically requires years of clinical experience. Additionally, our model could support research platforms investigating emotional expression patterns across different subgroups of children with autism, contributing to a more nuanced understanding of heterogeneity within the autism spectrum and potentially leading to more targeted intervention approaches based on individual emotional expression patterns.
## 3 Simulated Study of Different Decoupling Techniques
### Placing AEs at 90\({}^{\circ}\) and 180\({}^{\circ}\)  
In Figure 13, \(S_{11}\), \(S_{12}\), and radiation patterns of the antenna configuration are shown for 90\({}^{\circ}\) and 180\({}^{\circ}\) placed AEs without/with CG. The radiation patterns of all MIMO antennas are almost the same, but the MIMO antenna with CG has some change in pattern shape. The \(S_{11}\) plot of all MIMO antennas is almost identical. With CG structure, isolation is very low in 90\({}^{\circ}\) placed AEs and very high in 180\({}^{\circ}\) placed AEs at lower frequencies of the UWB as the distance between different ports is higher in 180\({}^{\circ}\) placed AEs as compared to the 90\({}^{\circ}\) placed AEs. Table 3 compares the isolation results of two-port UMAs with AEs placed 90\({}^{\circ}\) and 180\({}^{\circ}\) opposite to each other.  
Figure 12: (a) \(S_{11}\), (b) \(S_{12}\), and (c) ECC.  
Figure 13: (a) AEs positioned 90\({}^{\circ}\) without/with CG, (b) AEs positioned 180\({}^{\circ}\) without/with CG, (c) radiation patterns of 90\({}^{\circ}\) placed AEs, (d) radiation patterns of 180\({}^{\circ}\) placed AEs, (e) \(S_{11}/S_{12}\) of 90\({}^{\circ}\) placed AEs, and (f) \(S_{11}/S_{12}\) of 180\({}^{\circ}\) placed AEs.
# On the Use of a Feedback Tracking Architecture for Satellite Navigation Spoofing Detection
## 2 Types of Spoofing Attacks and Countermeasures
This article focuses on ECADLL, which is a receiver-level signal processing anti-spoofing technique based on a multiple-DLL architecture.
## 2 Materials and Methods
### Classifiers
#### 2.5.3 Linear Discriminant Analysis (LDA)  
LDA is a classical linear discriminant analysis method. On a given training sample set, LDA attempts to project instances onto a straight line, rendering the projection points of similar instances as close as possible while keeping the projection points of different examples at the maximum distance [54]. When classifying a new sample, it is projected onto the line, and its category is determined according to the position of the projected point.
## 3 Results and Discussion  
### General Observations  
Figure 4 presents three groups of instantaneous flame images for NC-I, NC-E, and NC-D (\(L\) = 8 cm), respectively, at typical moments (50 th, 150 th, 250 th, 450 th, and 650 th frame) after ignition. The flocculent NC-I and NC-E samples exhibit similar combustion behaviors, which are significantly different from NC-D with strip structure. Preliminary comparative research on NC-I and NC-E has revealed that the alcohol humectants barely affect the physical microstructure and thermal decomposition of NC substrate [25]. The alcohol additives improve the stability of pure NC, resulting in an attenuated combustion process instead of a violent deflagration. As observed in Figure 4a,b, the flames rapidly cover the whole exposed surface of NC samples in less than 2 s, and then evolve into a relatively stable combustion, followed by a decay process with the decreasing burning area. Additionally, the flames appear to be faintly yellow, accompanied by considerable luminous rising sparks. In contrast, the flame of NC-D sample spreads over the burner surface more slowly within 6-10 s. The combustion proceeds with glaring white flames, and sparks can scarcely be observed, as shown in Figure 4c.  
During the combustion, the surface of the NC melts or decomposes at the front edge of the flame, producing combustible vapors. In the case of the alcohol additives, the disruptive combustion might  
Figure 3: Schematic of the experimental setup of ISO 5660 tests.  
occur, leading to release of the sparks. This results from the formation of bubbles of alcohol vaporizing in the molten NC which bursts, causing little droplets of NC to form and burn to form sparks. However, it does not happen with the higher boiling point plasticizer which evaporates to form the yellow soot flame. The effect of disruptive burning is well known in heavy fuel oil combustion containing water or alcohol [33; 34], which might be helpful in understanding the flame appearances of NC samples in Figure 4.
# Simulation of the Infiltration of Fractured Rock in the Unsaturated Zone
## 3 Discrete Fracture and Matrix Models (DFM)  
### Single-Fracture Physical Experiment for Model Validation (Model CUBE)  
An immiscible displacement experiment conducted by [PERSON] and [PERSON] [25] on a homogeneous sandstone block was selected for the validation of the new fracture-matrix discretisation and nonlinear transfer scheme because (1) its geometry and setup are simple and fully documented, (2) the properties of the rock matrix and the fluids are given, and (3) there are detailed measurements that we can compare our results with.  
The model CUBE is shown in Figure 5. It consists of a \(5\times 5\times 5\) cm sample of highly permeable Berea sandstone. Except for the bottom boundary that was used to model the fracture, the cube was coated on all sides with epoxy resin. A small gap (0.1 mm) between the block and the impermeable core holder was used to model the constant aperture fracture.  
The rock sample was dried in a vacuum oven. Then, water was continuously injected through the injection port on the bottom left of the model at a constant flow rate of 0.1 cc/min. Excess fluid was collected from the production port in the model's bottom right corner. To simulate the imbibition experiment, a mixed-dimensional triangular FE mesh was created (Figure 5b). Additional line elements along the bottom side of the block represent the fracture.  
Since [PERSON] selected a very small fracture aperture, its value is affected by the grains and pores of the free surface of the sandstone sample. To capture related aperture variations, the Brooks-Corey model was chosen to parameterise the fracture, albeit with a lower lambda-parameter (Table 1). As in the physical experiment, a constant-rate water source was applied on the fracture's left end, and constant atmospheric pressure on its right end. All other boundaries were treated as natural (no-flow boundary condition).
# Fiber Bragg Grating-Based Optical Signal Processing: Review and Survey
## 2 Review of Design Methods for Fiber Bragg Gratings
### Synthesis of Fiber Bragg Gratings from a Targeted Transmission Specifications  
Although FBGs mainly operates in reflection mode, FBGs working in transmission possess appealing advantages, such as the fact that no additional elements (e.g., an optical circulator) are required to detach the grating output from the input signal, hence reducing the cost and size of the processor unit. Besides, FBGs operating in transmission are more robust against errors induced in the fabrication process. This is due to the weak interaction between the propagating electromagnetic field and the grating when the electromagnetic field is simply transmitted. It has been observed that, in this case, imperfections in the grating structure are not \"impressed\" on the propagating wave [38]. On the other hand, the processing bandwidth of transmissive FBGs has been found to reach more than one order of magnitude that attained by reflective configurations. In reflection, the device bandwidth is restricted by the attainable spatial resolution of grating fabrication technologies for tailoring the apodization profile. To give a quantitative example, if the apodization profile is written with sub-millimeter resolution (which is typically feasible), the resulting FBG is limited to temporal resolutions of several picoseconds. In terms of spectral bandwidth, this corresponds to a few hundreds of GHz [5]. As explained in this section, FBGs in transmission permit a relaxation of the spatial resolution of the attainable grating apodization profile in a tailored fashion, enabling the synthesis of THz-bandwidth signal processors.  
The synthesis process for a targeted FBG operating in transmission starts from the specification of the target transmissive coefficient, \(H_{T}(f)\), (it may also start from the specification of the required transmission impulse response, \(h_{T}(t)\)), and must produce the required refractive index perturbation.
# Flow Table Saturation Attack against Dynamic Timeout Mechanisms in SDN
## 5 Evaluation
### Evaluation of the Attack Effectiveness
#### 5.4.3 Effectiveness against the Dropping Request Method  
Then, we evaluate the impact in the scenario that the controller drops the packet-in request when the flow table is full. We measure the total number of packet-in requests and the dropped packet-in requests generated by benign and attack traffic, respectively.  
The result is shown in Figure 16. After the flow table occupation reaches 100%, more than 68% of the packet-in messages are dropped. This result demonstrates that the attack can cause high packet loss and degrade the network performance. Another observation  
Figure 14: Packet-in requests by benign hosts and attackers in the direct-forwarding case.  
Figure 15: The impact of flow table saturation attack on network delay.  
is that more than 97% of the dropped packet-in messages come from benign traffic. This is because the packet-in rates of attack flows are lower than benign flows, so they are less likely to be dropped. This also indicates that from the defense perspective, it is hard to identify attack flows solely based on the packet-in messages.  
To demonstrate the impact on the network performance from the data plane's perspective, we measure the packet drop rate of benign service requests sent to Server-1. In each round, we measure the number of request retries before receiving a response. As shown in Figure 17, 11 of the 15 rounds of requests cannot receive immediate responses. The flow requests require an average of four additional retries to be delivered properly. The reason is that when the flow table space is saturated, the SDN controller has to decline the packet-in messages. Flow requests can only pass through the switch if the corresponding flow rules are deployed.
# Ensemble Classifiers for Network Intrusion Detection Using a Novel Network Attack Dataset +
## 2 Background
### Machine Learning
#### 2.2.2 Ensemble Learning  
Ensemble approaches utilize collections of ML algorithms to produce higher predictive performance than could be obtained from a single ML classifier [19]. The main idea of an ensemble approach is to combine several ML algorithms to exploit the strengths of each employed algorithm to obtain a more powerful classifier. Ensemble approaches are particularly helpful if the problem can be split into subproblems such that each subproblem can be assigned to one module of the ensemble. Depending on the structure of the ensemble approach, each module can include one or more ML algorithms. In the world of network attacks, since the signatures of different attacks are quite distinct from one another, it is normal to have different sets of features as well as different ML algorithms to detect different types of attacks. It is thus obvious that a single IDS cannot cover all types of input data or identify different types of attacks [20; 21]. Many researchers have shown that classification problems can be solved with high accuracy when using ensemble models instead of single classifiers [22].  
Ensemble models can be thought of as using various approaches to solve a specific problem. This resembles the process according to which patients diagnosed with a dangerous medical condition, such as a tumor, usually see more than one doctor to solicit different opinions on their case. This is a kind of cross-validation that increases the probability of receiving an accurate diagnosis. Likewise, the outputs of various ML classifiers in an intrusion detection problem can be combined to enhance the accuracy of the overall IDS. The main challenge in using ensemble approaches is how to choose the best set of classifiers to constitute the ensemble model and which decision function to use to combine the results of those algorithms [23]. Bagging, boosting, and stacking are the main three methods used to combine ML algorithms in ensemble models [24].
## 5 Simulation Verification  
In this section, the feature values corresponding to each fault type are input to the fault diagnosis model for verification, and the prediction error is reduced by setting the number of implied layers and the number of iterations. The optimal choice is achieved when the number of implied layers is 50 and the number of iterations is 2000, as demonstrated by a large number of simulations. By combining etime() and clock in MATLAB, the time difference between \(t_{1}\) and \(t_{0}\) in Figure 10 is calculated as the fault identification time. This paper uses the same experimental equipment, in the same data volume environment, in order to reduce the error in computing time brought by the computer hardware.  
To further verify the applicability of the method proposed in this paper, comparison experiments were conducted by setting different fault distances and different fault initial phase angles, and the experimental results are shown in Tables 2 and 3.  
According to Table 2, the fault identification accuracy decreases with the increase of the fault distance. When the fault distance is 60 km, the fault identification accuracy is  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \begin{tabular}{c} **Number of** \\ **Training** \\ **Samples** \\ \end{tabular} & \begin{tabular}{c} **Number of Test** \\ **Samples** \\ \end{tabular} & \begin{tabular}{c} **Fault** \\ **Distance/km** \\ \end{tabular} & \
\begin{tabular}{c} **Recognition** \\ **Accuracy/\%** \\ \end{tabular} &
\begin{tabular}{c} **Training Time/s** \\ \end{tabular} \\ \hline \multirow{4}{*}{150} & \multirow{4}{*}{50} & 5 & 98 & 134 \\  & & 20 & 96 & 148 \\  & & 40 & 96 & 167 \\  & & 60 & 94 & 181 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Fault distances.  
Figure 10: Fault identification based on IPSO-WNN.  
96% and the fault identification time is 181 s, which verifies the strong applicability and robustness of IPSO-WNN under different fault distances.  
In Table 3, it is evident that IPSO-WNN is equally applicable at different initial fault phase angles.  
In this paper, the recognition accuracy and recognition time of IPSO-WNN are compared with the currently used recognition methods: WNN, support vector machine (SVM), PSO-WNN, and SSA-SVM. The comparison results are shown in Table 4.  
Table 4 shows that IPSO-WNN has superior recognition accuracy and recognition time as compared to other algorithms, proving the superiority of the proposed method.  
In summary, this paper presents a method for identifying various types of transmission line faults using IPSO-WNN, which combines the time-frequency localization property of wavelet transform with the self-learning ability of neural networks. The method is verified through comparison experiments with different identification methods, different fault distances, and different fault initial phase angles. The results show that the proposed method has a high identification accuracy and a fast identification speed, which can meet the needs of actual working conditions. Overall, the proposed IPSO-WNN method is a promising approach for fault identification in transmission lines.
## 2 Materials and Methods
### Characterization
Additionally, the coatings' ability to resist acidic and alkaline conditions was evaluated using solutions adjusted to pH levels of 1 and 14, respectively, achieved with concentrated hydrochloric acid (HCI) and sodium hydroxide (NaOH). These soaked filter papers were placed on the coating surface under a glass cover for a 24 h duration. Subsequently, the glass cover and filter paper were removed, and any residual liquid on the coating surface was meticulously wiped off. Color and gloss assessments were carriedout concurrently with both the liquid water absorption test and the cold liquid resistance test to evaluate the coatings' aesthetic uniformity. In conjunction with these examinations, contact angle assessments were executed according to the ASTM D7334-08 standard [62] to analyze how different additives affect the coating's ability to repel water. Large-scale images were taken using a Nikon 60 mm lens with an aperture set to f/2.8 (Nikon Instruments Europe, Amstelveen, The Netherlands). Contact angle measurements were performed using the NIS-Elements Microscope Imaging software, specifically Windows Version 4.30.01. Droplets of demineralized water (5 \(\upmu\)L) were dispensed from a syringe, released from 2 cm above the surface. After resting on the coating for 60 s, each droplet was captured in a photograph, and the wetting angle was determined using the imaging software. To ensure statistical validity, 3 samples per series underwent 5 measurements each, facilitating a thorough analysis of the coating's surface wettability traits. Finally, the protective capability of the bio-based filler regarding abrasion resistance was examined using the scrub test. This experiment involved testing 5 samples per series (measuring 150 \(\times\) 80 \(\times\) 2 mm3) using an Elcometer 1720 Abrasion and Washability Tester (Elcometer, Manchester, UK), following the ISO 11,998 standard [63]. The test utilized the standardized sponge Tool 5 weighing 232 g. Importantly, unlike the conventional procedure, this test was conducted dry, without the use of a cleaning solution. This modification aimed to prevent the solution from penetrating the polymeric matrix and wood, potentially influencing the results. Mass loss from the coatings was monitored after 250, 750, 1500, and 3000 cycles, with a frequency of 37 cycles per minute. Furthermore, the impact of the abrasive test on the coating's aesthetic properties was evaluated through analysis of color, gloss, and roughness. Moreover, SEM observations were conducted at the conclusion of the test to evaluate the impact of the abrasive sponge on the morphology of the samples, specifically examining the role of the bio-based additive in mitigating the abrasive process.