esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Smart Homes
### Home Application Areas
#### Surveillance and Security  
The implementation of communication technologies for essential surveillance and home automation leads to a wide range of opportunities as well as technical challenges.  
Surveillance and Security System require a robust configuration in order to collect meaningful, reliable, and accurate data. As a result, they do have a need for adequate support for the QoS required by the delay-sensitive and bandwidth-intensive multimedia data that they currently do not display. These restrictions do not significantly impact delay-insensitive data acquisition but can have considerable consequences for the real-time surveillance or monitoring applications as they often lead to insufficient or improper measurements and erroneous event detection [36].  
Real-time multiuser multimedia applications like monitoring or surveillance using multiple cameras have recently begun to be proliferated over flexible and low-cost multi-hop wireless networks. In these types of multimedia systems, several sources share the limited network resources and together transmit the captured video streams to a remote central monitor [36].  
There are many recent studies dealing with security issues in a smart home infrastructure. [PERSON] _et al._ classified the main risks of interaction between entities in a smart home and smart grid environments and proposed promising security countermeasures given the specific security goals [37]. [PERSON] _et al._ proposed a smart system using both face recognition and sound localization techniques to identify foreign faces through a door phone [38]. [PERSON] _et al._ proposed a smart home safety handwriting recognition technology to confirm user identity and to manage door security using a recurrent neural network with associative memory [39]. [PERSON] _et al._ considered the architecture and design of a secure access gateway for Home Area Networks (HAN), so that real-time secure monitoring and control of the devices could be achieved through a smart phone [12].
# A Payload Based Malicious HTTP Traffic Detection Method Using Transfer Semi-Supervised Learning
## 4 Experiment  
### Experiment Setup  
#### 4.1.1 Dataset  
To conduct the experiments, we collected two raw datasets. One was an open dataset from FSecurify [44], which contained a large number of HTTP requests to a WAF (web application firewall). Only two types of HTTP requests were labeled (i.e., benign and malicious), and thus it did not discriminate among different types of malicious HTTP requests. The other one was collected from a honeypot server deployed by our lab during March, 2019. It contained 6465 HTTP traffic samples, including 879 benign samples, 614 SQL injection samples, 4093 XSS samples, and 879 directory traversal samples. To balance the data of different types, we resampled the above two raw datasets and form the following two datasets.  
(1) Dataset A: It contained 80,000 samples (including 40,000 benign samples and 40,000 malicious samples), which were randomly selected from the open dataset.  
(2) Dataset B: It contained 800 samples (including 200 benign samples, 200 SQL injection samples, 200 XSS samples, and 200 directory traversal samples), which were randomly selected from the honeypot dataset.
# Temporal Enhancement of Top-N Recommendation on Heterogeneous Graphs
## 4 The Proposed Model
### Meta-Path Embedding
#### 4.3.3 Meta-Path Aggregation  
Different meta-paths may have different importance because every user may have their own preferences. An individual user is also likely to make different choices and have different interactions with other entities. A good meta-path embedding includes the semantics of each meta-path and accurately distinguishes them. Therefore, the introduction of an attention mechanism is critical. Considering the impact of users and items on the importance of different meta-paths, we consider enhancing the representation of meta-path embeddings by inputting user and item embeddings into the attention mechanism. This structure is called a collaborative attention mechanism. It differs from self-attention precisely in that it considers not only the interaction and influence within each meta-path but also the influence of users and items on the meta-path. Since different meta-paths have different semantics and importance, we introduce a two-layer neural network for the calculation of the attention scores, which are given as follows:  
\[\lambda_{u,i,p}^{(1)}=\text{ReLU}(\mathbf{w}_{u}^{(1)}\mathbf{x}_{u}+\mathbf{ w}_{i}^{(1)}\mathbf{y}_{i}+\mathbf{a}^{(1)}) \tag{20}\]  
\[\lambda_{u,i,p}^{(2)}=\text{ReLU}(\mathbf{w}^{(2)}\lambda_{u,i,p}^{(1)}+ \mathbf{a}^{(2)}) \tag{21}\]  
where \(\mathbf{w}_{u}^{(1)}\) and \(\mathbf{w}_{i}^{(1)}\) are the weight matrices for users and items, respectively; \(\mathbf{a}^{(1)}\) is the bias vector in the first layer; \(\mathbf{w}^{(2)}\) and \(\mathbf{a}^{(2)}\) are the weight vector and the bias vector, respectively, in the second layer; and ReLU is the activation function.  
The final attention weights can be acquired by normalizing the attention scores as follows:  
\[\lambda_{u,i,p}=\frac{\lambda_{u,i,p}^{(2)}}{\sum\limits_{a\in\mathbf{M}_{u,i }}\lambda_{u,i,a}^{(2)}} \tag{22}\]  
Finally, we obtain the meta-path embedding with the co-attention mechanism as follows:  
\[\mathbf{c}_{u,i}=\sum\limits_{p\in M_{u,i}}\lambda_{u,i,p}\cdot\mathbf{c}_{p} ^{\prime} \tag{23}\]
## 3 Method
### Model Overview  
The model architecture of the proposed method is shown in Figure 1. The proposed approach is divided into two phases. The first phase is introduced in Figure 1 and corresponds to Sections 3.2.1-3.2.5, and the second phase is for the final classification, corresponding to Figure 2 and Section 3.2.6.  
In Figure 1, the structure of the proposed method consists of three modules: (i) the visual feature generation module, consisting of a variational encoder \(Q\) and a variational decoder \(P_{i}\); (ii) a disentanglement module, consisting of encoder \(E\), decoder \(D\), relation module \(R\), and discriminator \(Dis\) (where \(R\) and \(Dis\) are the structures proposed by [PERSON] [10], which are not modified in this paper and therefore are not drawn in Figure 1); and (iii) a semantic decoder module, consisting of a semantic decoder \(Dec\). First, the feature generation module synthesizes the corresponding visual features using a conditional variational autoencoder (CVAE) [28] from the unseen classes semantic embeddings; this module uses visual features from seen classes for training. Then, a semantic decoder is used to decode the synthesized visual features from unseen classes and real features from seen classes and reconstructs them relative to the corresponding semantic space in order to ensure a better alignment between the synthesized visual features and corresponding semantics. Finally, the synthesized unseen classes' visual features and seen classes' real features are disentangled into semantic-consistent and semantic-unrelated visual features using disentanglement module encoder \(E\); here, the output of the semantic decoder \(Dec\) is fed into disentanglement decoder \(D\) to reconstruct the original visual features.
## 2 Materials and Methods
### Data Synthesis and Analysis  
Meta-analysis was accomplished utilizing R (Version 3.5.0; R Project for Statistical Computing). The mean difference (MD) together with the 95% confidence interval (CI) acted as the summary statistics. For the meta-analysis, a random-effects model was implemented. The level of significance was set at 0.05. To evaluate the variability among the studies, both the \(I^{2}\) static and the chi-square test were executed.
# A Universal Low-Complexity Demapping Algorithm for Non-Uniform Constellations
## 5 Performance Evaluation  
In this section, the complexity and BER performance of the Max-Log-MAP algorithm, QCSR algorithm, and SCSR algorithm are compared. The NUCs used in this section are 2D-256 NUCs and 1D-1024 NUCs in ATSC 3.0. The length of the LDPC code is 64,800 bits, and the CR is 2/15, 6/15, 10/15, 13/15.
# A Survey of Routing Protocols in WBAN for Healthcare Applications
## 2 Introduction of Wireless Body Area Network
### Application Field of a Wireless Body Area Network  
WBAN takes the human body as the application carrier, turns it into a part of the communication network, truly realizes the ubiquitous network and omnipresent service, and plays an important role in the following areas [5], as shown in Figure 2:  
* Medical care services and chronic disease surveillance: These are the most important applications. There has been no good solution for the prevention and treatment of chronic diseases, worldwide. The emergence of WBAN brings hope for the treatment and prevention of various chronic diseases. Through the real-time comprehensive analysis and processing of the collected physiological information, we can effectively prevent and treat the disease, before and during onset. We can also save the physiological information of the disease for subsequent diagnosis, treatment, and medical research. The most important thing is that patients can get rid of the trouble of wired monitoring and experience the convenient service of remote medical monitoring [6].  
Figure 1: Network architecture of a Wireless Body Area Network (WBAN).  
Assistance for special groups, such as the disabled and the elderly: Sensor devices are worn on the limbs of blind people, which can perceive the surrounding environment and road information, in real-time, to provide navigation and position services for blind people. Developing a care system for the elderly which can sense the actions of the elderly and make corresponding reminders, by locating the elderly, and record the activities to provide security and safety of the elderly.
* Other applications: Building human body network on athletes, real-time monitoring of athletes' physical condition, during training, and changing training intensity at any time; providing fire fighters with special environment monitoring (such as fire scene, toxic gas environment, etc.), warning firefighters regarding their own safety when their endurance threshold has exceeded; and, in the military, transferring important military intelligence to control centers or remote command troops, through wireless means. With the development of sensor technology and wireless network transmission technology, WBAN will be more and more widely used in medicine, including health monitoring, sports, entertainment, military, and aerospace fields, in the future. It also has considerable social value and economic benefits.
## 5 Simulation Results  
In this section, simulations for three cases, including the nominal system, perturbed system without the observer, and perturbed system with the observer are conducted to evaluate the performance of the proposed controller and disturbance observer for the hexacopter UAV system. Four control systems were designed to compare the proposed scheme with the conventional methods: namely, the standard BSC system (BSC); [PERSON]'s tracking differentiator [31; 32] based BSC system (YTBSC) studied by [PERSON] et al. [50]; [PERSON]'s tracking differentiator [28; 29] based BSC system (ZTBSC), where the adopted controller is designed according to those in [50]; and proposed tracking differentiator and super-twisting algorithm based BSC system (PTSBSC). The system parameters of the hexacopter UAV are listed in Table 1 of [10].  
To compare the performance of the tracking differentiators presented in Section 2, the parameters are selected as \(R=2\), \(r_{1}=1.5\), \(r_{2}=5.5\), \(a_{1}=1\), \(a_{2}=0.5\), \(\beta=0.5\), \(\gamma=0.5\) and \(\kappa=0.95\) under the initial condition of \(\alpha=0\), \(x_{1}(0)=2\), and \(x_{2}(0)=-2\). Comparative results are presented in Figure 2, where the proposed differentiator shows more rapid convergence performance than others. Only three tracking differentiators are considered, excluding the tracking differentiator proposed by [PERSON] et al. [27] due to its lower performance.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Quantity** & **Value** \\ \hline \(m_{0}\) & nominal mass of the body & 1.5 kg \\ \(l\) & length of arm & 0.275 m \\ \(J_{x}\), \(J_{y}\), \(J_{z}\) & moment of inertia at each axis & \(3.259\times 10^{-2}\), \(3.259\times 10^{-2}\), \(6.059\times 10^{-2}\) kg\(\cdot\)m\({}^{2}\) \\ \(J_{r}\) & moment of inertia at propeller & \(9.9\times 10^{-5}\) kg\(\cdot\)m\({}^{2}\) \\ \(C_{d}\) & coefficient of drag & \(1.523\times 10^{-7}\)Nms\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Selected values of the hexacopter UAV.  
Figure 2: Comparative results for three tracking differentiators. (**a**) \(\chi_{1}\). (**b**) \(\chi_{2}\).  
Figure 3: _Cont._
## 5 Experimental Setup
### Selection of the Pre-Trained Neural Network  
The work presented in [58] provides a comprehensive overview of challenges associated with pre-trained models. Neural network layers are distinctly assigned specific responsibilities: the early layers encapsulate general properties, while the middle layers focus on identifying shapes or task-specific characteristics. Transfer learning introduces a distinctive feature wherein only the end of the pre-trained network is retrained, while the other layers remain frozen. This approach enjoys widespread adoption in deep learning, owing to its advantageous features, including accelerated training times, enhanced performance, and a relatively modest data requirement.  
When selecting a neural network for a specific application, the key consideration is not identifying the optimal model, but rather determining the architecture and configuration that strikes the best balance between accuracy and speed [59].  
The \"SSD w/MobileNet, Lo Res\" model offers excellent speed coupled with a moderate average precision compared to alternative models. This particular model, characterized as a Single Shot Detection Convolutional Neural Network, excels in detecting multiple objects within a single image with the added benefits of low power consumption and simplified computations, rendering it apt for deployment in embedded devices. Given the real-time operational requirement of the planned system, prioritizing speed was of paramount importance, leading to the deliberate selection of this neural network.
## 3 Interference Mitigation Schemes
### MAC Approach
#### 3.2.9 Two-Layer MAC  
Two-layer MAC (2L-MAC) is the two-layer interference mitigation for WBASNs to ensure that the QoS requirement is met [33]. The algorithm is described in two phases: polling with backoff mechanism and channel switching.  
In this algorithm, the hub of a WBASN always checks for the idle channel before polling its sensor nodes. It then assigns the channel to the sensor nodes for intra-WBASN traffic by TDMA-based polling. Inter-WBASN interference can be avoided by a carrier-sensing mechanism between hubs. The QoS requirement is defined in the IEEE 802.15.6 standard as well as the contention window (CW) and backoff timer (BT) [1].  
In the polling with backoff mechanism, the hub performs carrier-sensing CCA to check the channel state before sending an intra-WBASN poll frame. In case 1, the channel is idle, and the hub sends a polling frame to the sensor nodes. When nodes receive the polling frame, they send data to the hub within the SIFS period. In case 2, the channel is busy, and the hub performs the backoff procedure and waits until the channel is idle. The hub knows the priority of each node in the WBASN and therefore sets the CW size based on the priority value. The hub chooses a value of BT and waits until the channel becomes idle. When the hub senses the idle channel status, the BT is decreased by one in each time slot. When the BT is zero, the hub sends a polling frame to the sensor node and waits for data from its sensor nodes. Figure 7 shows an example of the polling with a backoff mechanism.  
In the channel switching mechanism, each sensor must wait for a delay time of \(T_{delay}\) that is the time period from when its wake up event is triggered to when it receives a polling frame from the hub. If \(T_{delay}\) is longer than the predefined threshold \(H_{switch}\), both the hub and the sensor switch from the current operating channel to a backup channel; otherwise, a backup channel is chosen by the hub at the previous polling frame. A WBASN performs channel switching based on a channel sequence that is calculated using a 16-bit Galois linear feedback shift register function defined in [1].  
Figure 6: The superframe in RIC.
## 3 Simulative Experiment and Discussion
### Discussion  
This paper investigates the transformation of coordinates from plane coordinates to spherical coordinates. The accuracy of positioning was enhanced in the x and y directions, while some fluctuations were observed in the z-direction. The inclusion of an altimeter in the navigation system can effectively measure altitude, thereby complementing the entire navigation process. Furthermore, the experimental results establish a relationship between flight height and the field of view of the visual sensor at a specific central angle of the Earth. These findings offer valuable theoretical support for the autonomous flight of future aircraft. The specific discussions are as follows:  
1. This spherical model is a more realistic representation of the Earth and using the spherical model in the PNP (Perspective-n-Point) method provides more accurate positioning information for the aircraft. However, the spherical model is complex and challenging to use in practical applications due to sensor limitations. This paper derives the error propagation relationship of the spherical model and calculates corresponding central angle thresholds for errors of 1 m, 10 m, and 100 m. This simplifies the approach for engineering applications.
2. The paper presents the functional relationship between flight altitude and camera field of view. By considering different error ranges, the sensor field of view can be determined in conjunction with the flight altitude. Conversely, based on the  
Figure 8: Angles, errors, average errors, and variance diagram for the precisions of 1 m, 10 m and 100 m. (**a**) Precision of 1 m. (**b**) Precision of 10 m. (**c**) Precision of 100 m.  
given sensor field of view and error requirements, the accurate flight altitude can be determined, providing convenience for practical applications.
3. The experimental results demonstrate that the central angle threshold can be accurately and stably determined on the X and Y axes. However, there is fluctuation and simplicity on the Z-axis, indicating a limitation. Engineering applications can utilize complementary sensors such as altimeters to address this issue and it also serves as a key aspect for further research.
## 3 Preliminaries
### Formal Security Models  
#### 3.6.1 BAN Logic  
Burrows-Abadi-Needham (BAN) logic [14] is a widely used modal logic of belief specifically designed for the formal analysis of authentication protocols. Its primary purpose is to determine whether the principals involved in a protocol can logically deduce each other's identities, establish shared secrets, and believe in the freshness of the exchanged messages and keys. BAN logic operates on idealized versions of protocol messages and relies on a set of initial assumptions and inference rules. Key constructs and some relevant rules, as often cited in similar security papers [24; 28], include the following:  
* **Beliefs**: \(P\mid\equiv X\) (principal \(P\) believes statement \(X\)).
* **Sees**: \(P\triangleleft X\) (\(P\) sees/receives message \(X\)). Once Said: \(P\mid\sim X\) (\(P\) once said \(X\)).
* **Freshness**: \(\#(X)\) (formula \(X\) is fresh, e.g., contains a fresh nonce or timestamp).
* **Shared** secret key: \(P\xleftrightarrow{}Q\) (\(P\) and \(Q\) share a secret key \(K\)).
* **Jurisdiction**: \(P\mid\Rightarrow{}X\) (\(P\) has jurisdiction over statement \(X\)).
* **Message-meaning rule** (for shared keys): If \(P\mid\equiv P\xleftrightarrow{}Q\) and \(P\triangleleft\{X\}_{K}\), then \(P\mid\equiv Q\mid\sim X\).
* **Nonce** verification rule: If \(P\mid\equiv\#(X)\) and \(P\mid\equiv Q\mid\sim X\), then \(P\mid\equiv Q\mid\equiv X\).
* **Jurisdiction** rule: If \(P\mid\equiv Q\mid\Rightarrow{}X\) and \(P\mid\equiv Q\mid\equiv X\), then \(P\mid\equiv X\).  
BAN logic helps identify logical flaws in protocols but does not prove security against all types of attacks (e.g., computational attacks). It is often used as a first step in formal analysis.
# A Rapid Verification System for Automatic Emergency Braking Control Algorithm of Passenger Car
## 5 Discussion
Based on the above reasons, we suggest that when the AEB control algorithm needs to be modified repeatedly or the test scenario needs to be updated continuously, SIL testing is preferred. HIL testing is more suitable for AEB control algorithm verification. For example, when relative distance is used as the control parameter, the HIL testing results have higher confidence. If conditions permit, it is recommended to compare and verify SIL testing and HIL testing to improve reliability.
## 4 Results
### Fundamental Frequency Estimation  
The analysis of fundamental frequencies constitutes the first step in comparing the two studied models. These frequencies, which are intrinsic characteristics of the system and depend on factors such as stiffness and mass, allow us to determine if the modeled systems are similar.  
In the OpenFAST model, the monopile-OWT system's fundamental frequency is obtained from the fore-aft accelerations at the tower top under parked conditions and without environmental loads. The 400 s of simulation are used to obtain the response for both fixed-base and flexible-base conditions. The Power Spectral Density (PSD) is shown in Figure 2, where the peak values of the fundamental frequencies are indicated  
\begin{table}
\begin{tabular}{c c c} \hline
**Scenario** & **Wind Conditions** & **Wave Conditions** \\ \hline E-1 & NTM at \(U_{\text{R}}\) & ESS-1 yr \\ E-2 & ETM at \(U_{\text{R}}\) & EWH-50 yr \\ E-3 & EOG at \(U_{\text{R}}\) & EWH-1 yr \\ E-4 & EOG at \(U_{\text{out}}\) & EWH-50 yr \\ \hline \end{tabular}
\end{table}
Table 6: Environmental load scenarios extracted from [PERSON] et al. [6].  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Values** \\ \hline Turbulence models & NTM, ETM, and EOG \\ Wind velocities & 5, 10, 15, 20, and 25 m/s \\ Wind spectra & IEC Kaimal spectrum \\ Wind shear & Power law exponent \\ \hline \end{tabular}
\end{table}
Table 5: Wind-only scenarios.  
by dashed lines. On the other hand, as described in Section 2.1.1, the simplified model allows for the estimation of the fundamental frequency of the system directly by solving the eigenvalue problem.  
The analysis of the fundamental frequency reveals that its values are consistent across both models. The fixed-base model shows a fundamental frequency of 0.1724 Hz for the simplified model and 0.1717 Hz for OpenFAST, with a difference of \(-0.41\%\). Similarly, the flexible-base model shows a fundamental frequency of 0.1619 Hz for the simplified model and 0.1617 Hz for OpenFAST, with a difference of \(-0.12\%\). These minimal differences indicate that both structural models present, at least for their first mode, similar dynamic characteristics. Additionally, these results demonstrate that the SSI effects are equally captured by both models. In the studied problem, the effects of the foundation's flexibility produce a variation of approximately \(-6\%\) with respect to the fixed-base fundamental frequency, exceeding the \(5\%\) safety margin considered by recommended practices [3].