esa-sceva/satcom-chunk-collection huggingface dataset:
# Comprehensive Study of IoT Vulnerabilities and Countermeasures
## 2 Preliminary Materials
### Commonly Used Protocols
#### 2.7.3 WiFi  
WiFi, or IEEE 802.11, is a standard of wireless LAN technology widely used in business and home environments to obtain fast and reliable Internet access. WiFi provides a common platform to connect various devices, from smart home applications to industrial sensors. The range, reliability, and security strengths of WiFi are ideal for many IoT applications [22]. WiFi encryption has evolved in recent years, and each new generation provides stronger security against attacks. WEP was the original WiFi security protocol, which used the RC4 algorithm with two sides of data communication. However, WEP is easily cracked, and it is no longer considered secure. WPA was an improved version of WEP and addressed some of the security vulnerabilities in WEP. WPA2 is the successor to WPA and is considered to be more secure. WPA2 uses the Advanced Encryption Standard (AES) cipher [23]. WPA3 is the latest WiFi security protocol, offering the strongest security to date. WPA3 uses the Simultaneous Authentication of Equals (SAE) protocol, designed to be more secure than the four-way handshake used in WPA2 [24]. IEEE 802.11 is a living standard and new generations are being developed regularly to meet the growing demands of wireless networking. Some of the most used IEEE 802.11 standards are IEEE 802.11g, which operates in the 2.4 GHz band and supports data rates up to 54 Mbps; IEEE 802.11n, which operates in the 2.4 GHz and 5 GHz bands and supports data rates up to 600 Mbps.; IEEE 802.11 ac, which operates in the 5 GHz band and supports data rates up to 6.93 Gbps; and IEEE 802.11 ax, which operates in the 2.4 GHz and 5 GHz bands and supports data rates up to 9.6 Gbps [25; 26].
in angle-domain so that the fault characteristics of bearing defects are not affected by the rotating speed. As the most popular algorithm in order tracking, the computational order tracking method has a series of assumptions for the sampling process, which leads to the feature extraction error. In recent years, another widely used method deep neural network (e.g., convolutional neural networks (CNN) [13], stacked autoencoder (SAE) [14], deep belief network (DBN) [15]) has been proved to be advantageous and feasible in fault pattern recognition [16; 17; 18; 19]. Different from traditional methods based on physical meaning of signal, deep neural networks construct an end-to-end fault diagnosis system and adaptively extract fault features. However, the neural network does not know which fault features do not change with operation conditions and which are the nuisance features. Therefore, the feature extraction process cannot extract obvious fault information from the original features with mixed operation condition attributes [20].  
Nuisance attribute projection (NAP) is a method of eliminating channel interference for solving channel mismatch problem in speech recognition at the very beginning [21]. It was widely used in speaker recognition, face recognition and image recognition [22; 23; 24]. [PERSON] [25] introducedNAP to rolling bearing fault diagnosis and verified its effectiveness in performance degradation assessment and fault pattern recognition. In fact,NAP minimizes the value of the interference attribute by projecting the feature into another space to increase the proportion of fault feature [26]. However, when solving the projection matrix, it is complex that the minimization problem transformed into solving eigenvalues and eigenvectors problem. In addition, whenNAP is applied to fault diagnosis, it has massive diagnostic steps that are not a simple and efficient method. In this paper, based on previous research, a new approach that appliesNAP to CNN is put forward, and the objective function ofNAP is added to CNN for solution. The proposed method simplifies theNAP solving process and makes the diagnosis steps much clearer and more efficient.  
This paper is organized as follows. In Section 2, CNN andNAP are briefly described. Section 3 details the proposed learning method CNN-NAP. In Section 4, the proposed method is applied to the diagnosis of variable speed bearing data sets. In Section 5, the proposed method is compared with some traditional methods. Finally, conclusions are drawn in Section 6.
## 5 Quality Attribute Outcomes
### Privacy and Security
#### 5.1.3 Smart Contracts and Access Control  
Smart contracts on blockchains provide automated access control, enforcing security policies based on predefined conditions and limiting data access to authorized users. In the metaverse context explored by [PERSON] et al. [31], smart contracts enable secure, conditional access to sensitive healthcare and fitness data. This setup helps prevent unauthorized access to patient data and ensures that only authorized healthcare providers and analysts can interact with the information, adding a layer of security to virtual health and fitness environments.  
In their study, [PERSON] et al. used smart contracts to manage access permissions and effectively implement fitness-related recommendations. The framework includes a smart contract-based real-time inference engine that automatically generates recommendations based on specific criteria. For example, when a threshold heart rate is reached in an applicant, the smart contract is activated to issue appropriate recommendations [16].  
[PERSON] et al. [26] also use smart contracts to verify users' completion of physical activity challenges, automatically triggering reward distribution. This approach allows users to securely self-authenticate through the platform, promoting safe engagement in health activities without compromising privacy through unnecessary data exposure.
# An Embedding-Based Approach to Repairing OWL Ontologies
###### Abstract
Two algorithms, namely the threshold-based algorithm and the signature-based one, are designed to repair ontologies according to detected conflicts and axiom embeddings. To verify our proposed approach, we compared existing repair methods over 20 real-life incoherent ontologies. The experimental results indicate that the embedding-based algorithms could achieve promising performances. In addition, we discuss the advantages of each algorithm and provide recommendations for users to choose a suitable repair algorithm or ranking strategy. The main contributions of this paper are summarized as follows:  
* An embedding-based approach to repairing ontologies is proposed by considering both the syntax and the semantics of axioms in an ontology, and three metrics are defined to rank axioms based on the embeddings of axioms. * A threshold-based algorithm and a signature-based algorithm are proposed to instantiate our embedding-based approach. Both of them can be configured with different pre-trained models and various thresholds. * Abundant experiments have been conducted over 20 real-life incoherent ontologies. We implement our algorithms and existing repair algorithms using four ranking strategies. The experimental results show that two embedding-based algorithms could enhance the effectiveness of the traditional signature-based ranking strategy, and the threshold-based algorithm \(\mathsf{ThrCosAlg}\) with model \(\mathsf{m4}\) is able to remove fewer axioms and differentiate various axioms. The rest of the paper is organized as follows. Section 2 introduces the background knowledge about description logics and word embedding. Section 3 presents our general approach. Specific algorithms to instantiate the approach are proposed in Section 4. Section 5 shows various experimental results. Related works are introduced in Section 6, followed by conclusions and future works in Section 7.
# Flow Table Saturation Attack against Dynamic Timeout Mechanisms in SDN
## 4 Improved Saturation Attack Method
### Maintaining Phase  
In this phase, the attacker keeps generating traffic to keep the triggered flow entry active. The attacker can generate flows with packet intervals smaller than the timeout at a low cost. These packets make the triggered entries continuously match, so the rules will never expire due to idle timeouts.  
Then, we discuss how to make the attack pattern more stealthy and evade the detection of existing defense methods. In recent methods, the packet interval and flow duration are used to identify suspicious flows [29; 30; 31]. Their assumptions are based on a simple attack behavior, in which the attacker generates attack traffic at a minimum rate. To cope with this, we can customize the packet intervals and flow rates in this phase. The attack flows use different interval values and various interval patterns (e.g., random packet intervals) instead of fixed intervals to evade detection. Moreover, we can randomize the packet header fields and length of payloads to disguise packets as long as they can hit the corresponding flow rule. Furthermore, to prevent the injected flow entries showing similarity in terms of the timeout value, we can generate flow rules with different timeouts in the previous triggering phase. For random timeouts, we can deliberately generate rules with small timeouts; For adaptive timeout policies, we leverage the state transition diagram to inject flow rules with smaller timeouts. After that, we can also use shorter packet intervals in this phase to hit the flow entries. In this way, attackers can avoid detection. In addition, the controller acquires flow duration via statistical results. We can also resend packets soon after a flow rule expires to refresh its statistic of flow duration to evade possible defense. While these attack patterns can increase the cost for attackers, it should be noted that implementing these adjustments for attackers is not particularly difficult.
# Application of Angle Related Cost Function Optimization for Dynamic Path Planning Algorithm
## 4 The Spatial Optimized Dynamic Path Planning Algorithm
### Control the Scale of the Road Network
#### 4.1.2 Sub-Net Query Optimization Based on the Spatial Index  
In order to optimize and control the size of the network, an effective method is also needed to quickly match the appropriate sub-nets from all sub-nets once the location of the initial point of navigation has been determined. If the matching time of sub-net is too long, it will directly affect the solution time of the algorithm. Since all sub-nets in this study were stored in spatial databases, the sub-net matching problem was how to query spatial objects efficiently in spatial databases.  
In spatial databases, spatial query efficiency is an important indicator to measure their performance [21]. Because of the huge amount of data processed by spatial databases and the complexity and diversity of spatial objects processed, in order to deal with these complex data more efficiently, queries are often optimized by using spatial indexes.  
In this paper, we used the R-Tree index as the spatial index. The R-Tree index is an object-based indexing method. The R-Tree index divides the geographic space according to the Minimum Bounding Rectangle of the spatial object. The spatial index information of the object is organized into a tree structure, and the index information of the spatial object is managed by the index tree.
## 4 Experimental Study
### Tested Benchmarks
#### 4.3.3 Taguchi Method for Orthogonal Experimental Design  
Comparative studies were conducted using the Taguchi method [83] to analyse the sensitivity of the parameter design of the proposed DB-CSA and DB-CSA-II algorithms.The Taguchi method was provided by [PERSON] in 2004 and aims to analyse the sensitivities of user-defined parameters and propose a reasonable combination of parameter designs, using the orthogonal arrays (OAs) mechanism. The Taguchi method aims to minimize the number of runs that are needed for the experimental study. OAs are denoted by \(L_{a}(b^{c})\), where \(a\) is the number of experimental runs, \(b\) is the number of levels of each factor, \(c\) is the number of columns in the array, and \(L\) denotes Latin square design. In this study, the DB-CSA algorithm consists of six key parameters with an array of two-level factors. * The swarm size is fixed with two-level factors (\(N\)), for DMOPs \(N\in\{100,200\}\) and MoPs \(N\in\{100,300\}\). * The maximum number of iterations is fixed with two-level factors, for DMOPs \(T_{max}\in\{250,350\}\) for severe and moderate change and for MoPs \(T_{max}\in\{100,25,000\}\). * Both parameters of the Beta-1 function (p1) and (q1) are fixed to 5 or 50. * Both parameters of the Beta-2 function (p2) and (q2) are fixed to 5 or 50. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & \multicolumn{3}{c}{**Comparative Study (2) for DMOPs**} \\ \hline Comparable MaOEAs & Thirteen MOEAs [55] & Seven MaOEAs [54] \\ \cline{2-3}  & MSOPS-II [21] & \\  & MOEA/D [13] & \\  & HypE [27] & \\  & picea-G [41] & \\  & SPEA/SDE [39] & \\  & GrEA [35] & \\  & NSGA-III [24] & SPEA2/SDE [39] NSGA-II/SDR [53] \\  & KnEA [40] & MaOEA/IDG [31] \\  & RVEA [22] & VaEA [52] \\  & Two\_Arch2 [41] & \\  & \(\theta\)-dea [36] & \\  & MOEA/DD ] [25] & \\  & AnD [55] & \\ \hline Quality indicators & IGD & IGD \\ \hline Benchmarks & WFG, MaF & WFG, DTLZ \\ \hline Number of Objectives (M) & 2, 3, and 7 & 3, 5, 8, 10, and 15 \\ \hline Population size & 100 & 92, 224, 164, 280, and 152 \\ \hline Max-Iteration (Max\({}_{iter}\)) & 25.000 & 300: WFG1-9 and DTLZ 2,4,5,6, \\  & 1000: DTLZ 1,3,6 \\ \hline Independent runs & 31 & 30 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Parameters Settings and MaOEAs Solvers used for the comparative study (2).
# A Combinational Buffer Management Scheme in Mobile Opportunistic Network
## 2 Related Works
### Multiple Standards  
Most of the existing studies only focus on one property of the message in buffer management [8], and they do not consider the buffer from a multiple perspective. In recent years, several scholars try to solve the problem of buffer congestion from a comprehensive perspective. Compared with the single standard, the advantage of considering the composite properties of a message is that each property of the message occupies a certain percentage when calculating the utility value, thus fully demonstrating the overall characteristics of the message. For example, [PERSON] et al. present a comprehensive-integrated buffer management (CIM) strategy [8], which takes all information relevant to message delivery and network resources into consideration. The strategy calculates the utility value of the package from the following aspects: the lifetime of the message, the size of the message, and the number of forwarding hops. In the opportunistic network, we also consider the properties of the nodes because of the difference of the movement trajectories of the nodes. [PERSON] et al. [23] preferentially compute the utility value of caching data based on node interest and data transfer probability, and then design the overall cache management strategy including passive and proactive dropping policy, and scheduling policy. For instance, the author of [24] provides message priority information for the mobile vehicle by using the node encounter rate and context information, such as survival time, number of available copies and maximum number of forwarding message copies. However, this method is passive and nodes in the communication range can't initiatively select whether to receive the message from other nodes. In contrast, our proposed buffer management strategy enables the node to proactively select and receive messages from other nodes.
# Assessing the Security and Privacy of Baby Monitor Apps
## 5 Mobile App Analysis
### Privacy Leaks  
By intercepting and decrypting all network connections during a baby monitoring session using a transparent proxy, we got insights into the communication destinations and the actual transmitted data of each monitoring app. In the following, we analyze the data collected for privacy leaks, i.e., the transmission of personal data without providing a legal basis in the privacy policy [7]. For this purpose, we performed a two-step network analysis. First, we evaluated all network connections by collecting communication destinations and the type of personal information transmitted. Second, we matched the identified destinations to companies by using DNS lookups of the domain names so that we were able to compare the collected data with the statements in the respective privacy policy. Since we did not attempt to reverse-engineer encoding or obfuscation attempts of data transmission, which we were unable to interpret, we did not consider those data in our evaluation. However, we identified multiple privacy leaks, which aligns with the results of previous research exploring mobile applications for mHealth or childcare [11; 15].  
As indicated in Figure 5, there are immense differences in the number of domains and companies contacted and data exchanged between the apps. The Dormi app transfers  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**App** & **Video Exchange** & **Domain/TP** & **Server Owner** \\ \hline Dormi & Relay & 34.68,30.36 & Google LLC \\ BabyCam & Client/Server & Local Router & User (App) \\ Saby & Relay & \(\omega\)/24.24/2.14 & Xirsys \\ Babyphone Mobile & Relay &
\begin{tabular}{l} beyond1,9 perameier.com \\ (194.50.15.124) \\ \end{tabular} & Papermeier Software UG \\ \hline \hline \end{tabular}
\end{table}
Table 7: Network infrastructure used for exchanging video footage over the internet.  
Figure 4: Network communication of the BabyCam app.  
personal data using Google server infrastructure, but does not mention this in the privacy policy, which we consider a privacy fault. More severely, the BabyCam app states that it collects cookies and tracking elements for use with Google AdSense, but exchanges more than the declared data with numerous advertising companies (see Table 8). Similarly, the Saby app does not mention data exchange with the AppsFlyer Inc. company. The only app without identified privacy leaks is the Babyphone Mobile app.  
Figure 5: Captured network connections.
# Spatial Prediction of Soil Contaminants Using a Hybrid Random Forest-Ordinary Kriging Model
## 3 Results  
### Eda  
Table 3 summarizes the descriptive statistics of the collected heavy metals. Except for one maximum value, all the remaining data points were below 500 mg/kg. The standard deviation was calculated to be 183.63 mg/kg. The data exhibited a positive skewness (2.37), as indicated by the mean value (138.55 mg/kg) being higher than the median, suggesting that the use of raw data for predictions could result in biased outcomes. Therefore, a log transformation was applied to normalize the data before interpolation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multicolumn{2}{c}{**Option**} & \multicolumn{2}{c}{**Parameter**} \\ \hline
**Variogram** & **Coordinate** & **No. of Lags** & **Weight** & **No. of** \\
**Model** & **Type** & & & **Closest Points** \\ \hline Gaussian/ & & & True & \\ Spherical/ & Euclidean/ & Min 2 & Min 0.1 & \\ Exponential & Geographic & Max 20 & Max 1.0 & \\ Step 2 & Step 0.1 & & Step 1 \\ \hline \end{tabular}
\end{table}
Table 2: Settings of the Ordinary Kriging (OK) options and parameter grid.  
Figure 3a shows the initial distribution of heavy metal concentrations used in this study. Figure 3b illustrates the distribution of data values after log transformation. Figure 3c shows a Q-Q plot, demonstrating that the data follow a normal distribution after log transformation. After inspection, the log-transformed sample data do not deviate significantly from normality.
## 2 Myocardial Disease  
The absence of a strict definition and the differences in availability of HAART between developed and developing countries make it difficult to estimate the incidence and prevalence of HIV-cardiomyopathy. This explains the varying results reported in the literature [2].  
In the pre-HAART era, HIV-associated cardiomyopathy was primarily characterized by a dilated left ventricle (LV) with symptomatic systolic dysfunction (Central Figure). Most often, the underlying cause was repetitive myocarditis, which was related either to opportunistic infections, direct viral cytotoxicity, nutritional deficiencies, autoimmunity, or severe immune suppression [3].  
One of the earliest studies on HIV-associated cardiomyopathy, conducted by Levy et al., found that 16% of 60 enrolled patients had systolic dysfunction, with 66% of them diagnosed with acquired immunodeficiency syndrome (AIDS) [4]. De Castro et al. evaluated the incidence of heart pathologies in HIV-infected patients during different stages of the disease. Of the patients who were in the end stage, 17% presented a dilated cardiomyopathy and 6% presented myocarditis, while 14% had regional cardiac abnormalities [5]. Similarly, Herskowitz et al. prospectively studied 69 patients without clinical heart disease, to find a 15% prevalence of global left-ventricular hypokinesia [6]. De Castro et al. identified acute left-ventricular dysfunction in 8% of AIDS patients without previous heart conditions. Of these, 85% died from congestive heart failure, and 57% were diagnosed with myocarditis through histology [7].  
The introduction of HAART therapy increased life expectancy and reduced opportunistic infections. However, it also brought attention to other chronic HIV-related complications. As such, the presence of systolic dysfunction and especially the number of cases with severely reduced ejection fraction decreased, whereas diastolic dysfunction became more prevalent.  
The Study to Understand the Natural History of HIV/AIDS in the Era of Effective Therapy (SUN Study) examined 656 asymptomatic HIV-infected participants who underwent echocardiography between 2004 and 2006. It found that 26% had diastolic dysfunction, compared to 18% with systolic dysfunction. Risk factors for diastolic dysfunction included high-sensitivity C-reactive protein (hsCRP) levels and hypertension, while myocardial infarction history, elevated hsCRP, and smoking were associated with systolic dysfunction [8].  
A meta-analysis by Cerrato, which included 2242 HIV-positive patients with minimal symptoms, found a 44% incidence of diastolic dysfunction, with hypertension and older age as key predictors. Nearly all patients (98%) were on HAART, and cardiac imaging was performed at a median of 8.1 years post-diagnosis. The most common form of diastolic dysfunction was impaired relaxation (32%) [9].
# IW-NeRF: Using Implicit Watermarks to Protect the Copyright of Neural Radiation Fields
## 2 Related Work
### Watermark Algorithm for Neural Radiation Field  
The remarkable performance of neural radiance fields (NeRFs) in 3D reconstruction has garnered significant attention from researchers. [PERSON] et al. [13] introduced StegaNeRF, a method for embedding secret information in NeRF rendering. Their approach involved an optimization framework, initially training a standard NeRF model and subsequently conducting additional training to achieve the rendering of secret images while preserving the original visual quality. Similarly, [PERSON] et al. [35] embedded secret messages within the implicit representation function of masked data, enabling direct extraction of these messages via a shared key between the sender and receiver. In contrast, [PERSON] et al. [14] proposed an anti-distortion rendering scheme, replacing the original color representation in NeRF with a watermarked color representation to ensure stable extraction of watermark information in 2D rendered images. Additionally, [PERSON] et al. [36] utilized the newly synthesized view of NeRF as a pivotal element for copyright verification, employing parameterized methods to train a watermark extractor for model validation. Notably, the majority of the aforementioned schemes rely on watermark extractors for copyright protection, which may entail certain security risks. Consequently, the development of an implicit representation watermarking algorithm specifically tailored for NeRF models has been pursued to address these concerns.