esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### DNA Extraction and High-Throughput Sequencing Analysis  
Microbial DNA was extracted following the protocol provided by the E.Z.N.A(r) soil DNA kit (OmegaBio-Tek, Norcross, GA, USA). Subsequently, DNA quality, concentration, and integrity were evaluated using 1% agarose gel electrophoresis. The amplification of the V3-V4 region of the bacterial 16S rRNA gene employed primers 338F (5\({}^{\prime}\)-ACTCCTACGGGAGGGCAGCA-3\({}^{\prime}\)) and 806R (5\({}^{\prime}\)-GGACTACHVGGGWTTCTAAT-3\({}^{\prime}\)), while amplification of the fungal ITS gene sequences utilized primers ITS1F (5\({}^{\prime}\)-CTTGGTCATTTAGAGAGGAAGTA-3\({}^{\prime}\)) and ITS2R (5\({}^{\prime}\)-GCTGCTTCTTCATCGATGC-3\({}^{\prime}\)). PCR amplification followed the method outlined by [PERSON] et al. [35]. Following purification, quantification, and storage at \(-\)80 \({}^{\circ}\)C, amplification products underwent raw sequencing data filtering by Trimmomatic v0.33 software. Primer sequences were eliminated using cutadapt 1.9.1 software to obtain high-quality sequences (Clean Reads). Subsequently, sample clean reads were spliced using Usearch v10 software and length-filtered according to specific region length ranges. The data underwent the removal of low-quality and chimeric sequences and the classification of high-quality sequences. The OTU representative sequences were taxonomically classified utilizing the Silva 138 16S rRNA database ([[http://www.arb-silva.de](http://www.arb-silva.de)]([http://www.arb-silva.de](http://www.arb-silva.de)), URL (accessed on 23 April 2024)) and the Unite 8.0 ITS database ([[http://unite.ut.ee/](http://unite.ut.ee/)]([http://unite.ut.ee/](http://unite.ut.ee/)), URL (accessed on 23 April 2024)) for taxonomy annotation.
# Wind Speed Measurement via Visual Recognition of Wind-Induced Waving Light Stick Target
## 3 Wind Speed Calculation  
### Description of 'Light Stick'  
The stick is formed by enhancing the stiffness of an almost-rigid rope. The rope is made up of tightly weaved fiber materials with a transparent plastic aglet sheath attached to the end to keep the woven fibers from unraveling. To ensure that the stick remains straight and does not bend under wind load, a thin wire is run through the inside of the stick, serving also as a means of forming a revolute joint (Figure 10). The stick is 225 mm in length and 2.97 g in weight.
# A Chaotic Compressive Sensing Based Data Transmission Method for Sensors within BBNs
## 3 Preliminaries  
### Compressive Sensing  
Compressive sensing is a signal processing method that was proposed in [31]. It represents original signals in dimensionally reduced values that are called observational values. Suppose the original signal is \(s\in\mathbb{R}^{N}\), and it has a sparse compressible representation in a basis \(\Psi\in\mathbb{R}^{N\times N}\), that is \(x=\Psi s\), where \(x\in\mathbb{R}^{N}\) is a \(k\)-sparse vector, namely, there is at most \(k\) of its entries that are nonzero, and in this paper we consider that \(\Psi\) is an orthogonal matrix \((\Psi\Psi^{\mathsf{T}}=I_{N\times N},\Psi^{\mathsf{T}}\Psi=I_{N\times N})\). The compressive sensing process is then taken as  
\[y=As \tag{1}\]  
where \(A\in\mathbb{R}^{M\times N}\) (\(M<N\)) is the measurement matrix and \(y\in\mathbb{R}^{M}\) consists of measurement values, also named observational values.  
We then obtain  
\[y=A\Psi\Psi^{\mathrm{T}}s=A\Psi^{\mathrm{T}}x=\Phi x \tag{2}\]  
where the matrix \(\Phi=A\Psi^{\mathrm{T}}\) is the sensing matrix, and the sensing matrix should satisfy the condition proposed by [PERSON] in [PERSON] (1992, 1993).  
A matrix \(\Phi\) satisfies the restricted isometry property (RIP) of order \(k\) if there exists a \(\delta_{k}\in(0,1)\) such that  
\[(1-\delta_{k})\|x\|_{2}^{2}\leq\|\Phi x\|_{2}^{2}\leq(1+\delta_{k})\|x\|_{2}^{2} \tag{3}\]  
holds for all \(x\in\sum_{k}\), where \(\sum_{k}=\{x:\|x\|_{0}\leq k\}\) denotes the set of all \(k\)-sparse vectors in \(\mathbb{R}^{n}\).  
From Equation (3), we can also infer that, satisfying the rule of RIP with order \(2k\), the measurement matrix \(\Phi\) approximately preserves the distance between any two \(k\)-sparse vectors, which is essential to noise robustness ([PERSON] and [PERSON], 2016).  
There are many methods to reconstruct the original signal \(s\) from the measurement values, and orthogonal matching pursuit (OMP) ([PERSON] and [PERSON], 2016) is one of the simplest greedy approaches to accomplish CS reconstruction. The OMP algorithm first finds the column of \(A\) that is most correlated with the measurements and then repeats this step by correlating the columns with the residual signal, which is achieved by subtracting the contribution of a partial estimate of the signal from the original measurement vector.
## 1 Introduction
We intend to drastically reduce all those costs by providing a library of prototypical prompts for every segmentation task so we can let the specialists devote their time to other, more critical tasks since the segmentation process is considerably simplified. In addition, the hardware and personnel requirements to obtain these results are much lower than those needed to train a deep learning model from scratch or to modify the behavior of an existing one using model modification techniques. These results would go even further if, instead of segmenting all the slices of a volume, it was sufficient to segment just one slice, and the model would propagate the segmentation to the rest of the slices. In this paper, we try to answer two main research questions: (1) how much, if anything, does SAM 2 improve the performance of SAM in the medical image segmentation tasks? (2) Can we leverage SAM 2 video segmentation capabilities for medical image segmentation to reduce further the number of prompts needed to segment a volume of medical images? Since the publication of SAM, the scientific community has been interested in its performance in medical image segmentation tasks. Many papers have been published investigating its performance in this field. Studying the differences between SAM and SAM 2 when segmenting medical images can be a fundamental resource that other researchers can use to determine whether or not to use this new version in their research, given the vast amount of effort applied to build on SAM's segmentation capabilities in this direction. Before exploring whether we can leverage SAM 2's video segmentation capabilities to reduce the number of prompts needed to segment medical image volumes, we must first determine whether SAM 2 performs at least as well as SAM in the medical image segmentation task. If SAM 2 does not offer performance improvements, investigating its video segmentation capabilities would be irrelevant. While a comprehensive, modality-specific performance analysis of SAM 2 is beyond the scope of this study, we also include an exploratory evaluation that offers initial insights into its applicability and effectiveness across different imaging modalities and anatomical regions. The structure of this paper is organized as follows. In Section 2, we review the related works that have been published on the topic of medical image segmentation using SAM and compare them with our approach. Section 3 details the methodology behind our experiments, including the datasets utilized, the operation of segmentation with SAM 2, the preprocessing steps applied to images and videos, the data inputs required by SAM 2, and the metrics employed to evaluate segmentation quality. Section 4 compares the experimental results obtained using SAM and SAM 2 and assesses the new segmentation technique's performance. These findings are further discussed in Section 5, where specific aspects and nuances are explored in greater depth. Finally, Section 6 offers an in-depth analysis of the results and proposes potential directions for future research.
# Security Challenges for Users of Extensible Smart Home Hubs: A Systematic Literature Review
## 3 Results
### Synthesis of Key Security Challenges
#### 3.2.2 Insecure IoT Ecosystems and Integrations  
Extensible smart home hubs have a comparatively large attack surface given their support for multiple device ecosystems. All systems, standards, and protocols have underlying weaknesses. One concern is that the underlying weaknesses in the integrated components could have cascading effects on the attack surface of the hub. The potential underlying weaknesses of smart home devices are well described by the risk categories in OWASP IoT Top 10:2018 [35], an IoT-specific version of OWASP Top 10 [34]. The risk categories from OWASP IoT Top 10:2018 are shown in Table 6. Many of the included papers [29; 30; 32] describe vulnerable devices and underlying protocols as a potential security threat to hubs. Suggested solutions to this challenge include compartmentalizing components [1; 20; 29; 30], enforcing internal access control [23; 25; 27], analyzing firmware at runtime [30], or using anomaly detection technology to detect and prevent suspicious activity related to device integrations [32].  
Another important concern extends beyond vulnerabilities inherited from underlying protocols, instead focusing on integration bugs with security implications. This is especially relevant considering the added complexity of supporting multiple heterogeneous  
\begin{table}
\begin{tabular}{l l} \hline \hline \# & **Category** \\ \hline
1 & Weak, Guessable, or Hardcoded Passwords \\
2 & Insecure Network Services \\
3 & Insecure Ecosystem Interfaces \\
4 & Lack of Secure Update Mechanism \\
5 & Use of Insecure or Outdated Components \\
6 & Insufficient Privacy Protection \\
7 & Insecure Data Transfer and Storage \\
8 & Lack of Device Management \\
9 & Insecure Default Settings \\
10 & Lack of Physical Hardening \\ \hline \hline \end{tabular}
\end{table}
Table 6: OWASP IoT Top 10:2018.  
\begin{table}
\begin{tabular}{l l} \hline \hline \# & **Category** \\ \hline A01 & Broken Access Control \\ A02 & Cryptographic Failure \\ A03 & Injection \\ A04 & Insecure Design \\ A05 & Security Misconfiguration \\ A06 & Vulnerable or Outdated Components \\ A07 & Identification and Authentication Failures \\ A08 & Software and Data Integrity Failures \\ A09 & Security Logging and Monitoring Failures \\ A10 & Server Side Request Forgery (SSRF) \\ \hline \hline \end{tabular}
\end{table}
Table 5: OWASP Top 10:2021.  
ecosystems. [PERSON] et al. [24] and [PERSON] et al. [28] explain how assumptions about interfaces, APIs, and trust boundaries can cause invalid or insecure interactions between components. In addition, [PERSON] and [PERSON] [1] highlight the lack of proper guidelines for hub integration developers, which might add to this challenge. To solve this problem, [PERSON] et al. [24] demonstrated an approach that identifies integration bugs through interface specification extraction in the SmartThings hub.
## 6 Solutions for Security and Trust in IoV
### ML-Based Solutions for Trust
#### 6.3.3 Reinforcement-Learning-Based Solutions
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Citation** & **Year** & **Focused Area** & **Solution Technique** & **ML Type** & **Algorithm** & **Attack Type** & **Object** \\ \hline
[155] & 2018 & & SL & Bayesian neural network & Malicious node attacks & Extract relevant features from the vehicular network for a trust model \\ \hline
[156] & 2019 & & SL & XGBoost and RF & Suspicious activity & Detect suspicious activity \\ \hline
[157] & 2020 & & SL & KNN & Fake position attacks & Detect misbehaving nodes \\ \hline
[158] & 2020 & & SL & RF & Four types of attacks & Generated the trust factor \\ \hline
[159] & 2020 & & SL & NB & Malicious node attacks & Indicate the vehicles as trusted or untrusted \\ \hline
[160] & 2022 & & SL & Metaheuristic & Sybil attacks & To identify Sybil nodes and protect messages from Sybil attacks. \\ \hline
[161] & 2023 & & SL & KNN and SVM & Insider attacks in IoV & By computing parameters and identifying dishonest vehicles, the aim is to accurately evaluate trust \\ \hline
[162] & 2019 & & UL & RNN-based & Sybil attacks & The global detection of Sybil attacks \\ \hline
[163] & 2019 & & UL & DNN & Dishonest vehicle & Classify communications as either honest or dishonest \\ \hline
[164] & 2019 & & UL & Various UL models & Dishonest vehicle & Classify the cars as either honest or dishonest \\ \hline
[165] & 2022 & & UL & Bayesian and active detection methods & Malicious nodes & Enhance the reliability of communication through the evaluation of the trustworthiness of vehicles and events. \\ \hline
[166] & 2018 & & RL & Q-learning & Dishonest vehicle & Capable of resisting attacks and enhancing trust \\ \hline
[167] & 2019 & & RL & Q-learning & Malicious node attacks & Minimize the vehicle’s privacy gain \\ \hline
[168] & 2020 & & RL & Q-learning & Dishonest vehicle & Evaluate the trust value of the sender \\ \hline
[169] & 2023 & & RL & DRL & OBU attacks & Reduce attack motives and make informed decisions to improve OBU collaboration. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Trust solutions based on ML. objects, the recognition of driver monitoring patterns, vehicle theft, and compromised functional safety [57; 76; 171; 172].
# HERMES CubeSat Payload Thermal Balance Test and Comparison with Finite Volume Thermal Model
## 2 The HERMES Pathfinder Mission
### Demonstration Model  
Given the complex geometry of the PL and its strict operative temperature requirements, the correspondence between the numerical thermal model and the thermal behavior of the actual equipment shall be ensured by proper model parameter identification. The only way to achieve such confirmation is to perform a TBT using a Thermal Vacuum Chamber (TVAC) [3]. The goal of the TBT is to allow a PL model to reach different thermal steady-state conditions in a vacuum environment in order to observe the thermal behaviour of the system in response to different imposed boundary conditions. The Demonstration Model (DM) of the payload adopted for this purpose is fully representative of the Flight Model (FM) from a thermo-structural point of view, except it has no functional hardware, meaning that some electronics boards cannot be switched on to perform functional tests. Heat dissipation by electronics plays a major role in the determination of the system operative temperature and cannot be neglected: electric heaters are placed on the active components to impose the required thermal flux (more details on that are given in Section 3.2). Heaters are fundamental in order to assess how the electronics heat dissipation  
Figure 3: HERMES DM with TVAC interface. TVAC interface on bottom right.  
Figure 2: HERMES scientific payload insight. Electronics (**left**) and detector (**right**).  
affects the temperature fields of the PL. In particular, it is crucial to see how such heat dissipation affects the temperature of the SDDs. Indeed, in the DM, the SDDs, the crystals and the boards are arranged as in the flight configuration, thus all the conductive paths connecting the detectors with the electronics are identical to the actual PL (more details on the connections are given in Section 3.2 and in [20, 21, 22]). The DM also differs from the flight model because the atomic clock and the high-voltage DC/DC converter are absent, replaced with equivalent mass brass dummies (more details in Section 3.2).
# A Deep-Learning-Based Secure Routing Protocol to Avoid Blackhole Attacks in VANETs +
## 1 Introduction
### Related Works and Motivation
This can lead to substantial routing overhead and increased end-to-end delay. The authors in [26] modeled SSAE and softmax classifier deep network schemes for DDoS attack detection. The key features were extracted from a dataset and their dimensionality was reduced using the SSAE model. These selected features were then fed into a softmax classifier to detect attacks. Although this method significantly decreases training time, it does not enhance security when it is applied to hardware with limited resources. The authors in [27] proposed SVM for IDS. SVM is a well-known machine learning algorithm that is widely used for pattern classification problems. This method prevented the modification of classified events, but it did not include the large amounts of data obtained via vehicular communication to improve the SVM's performance. In [28], to improve security, the authors proposed the CMEHA-DNN deep learning-based intrusion detection model for the purpose of detecting Sybil attacks in VANETs. The Sybil attack is successfully identified by this method. These works [26; 27; 28] did not focus on blackhole attacks in VANETs. However, from the above works, we can conclude that machine learning techniques, especially DL, can be used to avoid attacks in VANETs. Table 1 shows the full comparison of the existing approach and our proposed approach to easily compare and identify the research gap of our study. It is evident that the existing schemes have numerous limitations. Thus, we propose a deep learning approach that is utilized in various aspects of networking, including clustering, routing, and blackhole detection which has not been explored by other authors in the previous studies. This leads to the enhancement of both the security and efficiency of network routing. By employing DL, we are able to leverage the inherent complexities of the data and patterns presented  
within the network, leading to more accurate results compared to conventional methods. The DL approach also can reduce delays in the optimization process and ultimately improve the performance of the overall system. Based on the above, three fundamental questions arise, which will be addressed in this article: (1) In VANETs, how do we protect the confidential packet from a blackhole attack? (2) Can the data-driven method be utilized instead of the iteration-based method? (3) Is it possible to manage mobile nodes to improve route stability?
# Performance Evaluation of Random Access Methods for IoT-over-Satellite
## 3 Method
### Access Models for IoT-over-Satellite
#### 3.3.1 Case I: Pure-ALOHA  
The simplest random access method is pure-ALOHA access where we assume that all the IoT devices transmit with no scheduling or channel sensing involved. This means that regardless of the satellite availability, IoT devices will wake up, transmit, and then go back to sleep. As such, we should expect low probability of success results from this completely random access method due to two reasons: (i) Satellites might be unavailable to receive the packet, and (ii) elevated interference due to packets overlapping. Case I in Figure 9 depicts the expected behavior of pure-ALOHA where the IoT devices transmit at any instance.
# Fusion of Land-Based and Satellite-Based Localization Using Constrained Weighted Least Squares
## 5 Proposed Method
### Robustness  
A total of 10 sources are randomly selected within the range of \([120^{\circ}\) E\(\sim\)130\({}^{\circ}\) E\(]\times[20^{\circ}\) N\(\sim\)30\({}^{\circ}\) N]. The localization accuracy of the proposed algorithm and the corresponding CCRLB distribution are tabulated.  
First, set \(\sigma_{h}=3\) km and \(\sigma_{s}=2\) km, and the corresponding results are shown in Figure 7. Then, set \(\sigma_{\theta}=1.5^{\circ}\), \(\sigma_{\varphi}=2.25^{\circ}\), and \(\sigma_{r}=0.5\) km, and the results are shown in Figure 8.  
As can be seen from Figures 7 and 8, both the RMSE and CCRLB increase as the error increases, and the distribution is more dispersed. In addition, the distribution of the RMSE is basically the same as that of the CCRLB, and the localization results do not  
Figure 5: The localization accuracy varies with localization parameter error.  
Figure 6: The localization accuracy varies with systematic error.  
produce abnormal distribution values, which indicates that the proposed algorithm has good robustness in terms of the source location.  
Figure 7: Distribution of RMSE and CCRLB with localization parameter error. ‘+’ is the outlier.
# A Hybrid Multi-Agent Reinforcement Learning Approach for Spectrum Sharing in Vehicular Networks
## 3 System Model
### Deep Q Networks  
In scenarios with limited observation space and a small lookup table, Q learning excels. However, challenges arise when confronted with larger state and action spaces, thus resulting in a growing lookup table and non-stationarity. The latter occurs when many Q values remain unchanged for extended periods, thus significantly slowing down the learning process after multiple repetitions. To overcome these challenges, DQNs introduce a paradigm shift by integrating Q values into a sophisticated neural network, which is termed the DNN, as illustrated in Figure 3. This network is characterized by the parameter set \(\theta\), thus representing its weights. The key concept involves approximating and determining these weights \(\theta\). Once established, the DNN efficiently addresses the mapping of the Q values, thereby enhancing stability in \(Q(s_{t},a_{t})\)[22].  
The Temporal Difference (TD) error, denoted by \(\delta\), is fundamental to the learning process and is defined as given in Equation (8).  
\[\delta=R+\gamma\max_{a^{\prime}}Q(s^{\prime},a^{\prime};\theta^{\prime})-Q(s,a ;\theta) \tag{8}\]  
where \(\delta\) represents the TD error, \(R\) is the immediate reward, \(\gamma\) is the discount factor (between 0 and 1), \(max_{a^{\prime}}Q(s^{\prime},a^{\prime};\theta^{\prime})\) represents the maximum expected future reward the agent can achieve from the next state \((s^{\prime})\), and \(Q(s,a;\theta)\) represents estimated Q value for taking the chosen action \(a\) in the current state \(s\), which is also based on the current policy parameters \(\theta^{\prime}\). The loss function used during training, denoted by \(L\), is based on the Mean Squared Error (MSE) between predicted and target Q values as given in Equation (9).  
\[L=\frac{1}{2}\Big{(}Q_{\text{target}}-Q_{\text{pred}}\Big{)}^{2} \tag{9}\]  
where \(Q_{\text{target}}\) is calculated using the TD error and represents the target Q value. \(Q_{\text{pred}}\) is the predicted Q value obtained from the DNN.  
Figure 3: Generalized structure of Deep Q Networks [23].
## 3 Experimental Results and Analysis
### Experimental Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method 1** & **Method 2** & \(p\)**-Value** & **Holm-Bonferroni Correction** \\ \hline TCA+ & CCA+ & 0.949 & 1.000 \\ TCA+ & KCCA+ & 0.005 & 0.054 \\ TCA+ & KSETE & \textless{}0.001 & 0.001 \\ TCA+ & GMOTDP & \textless{}0.001 & \textless{}0.001 \\ CCA+ & KCCA+ & 0.007 & 0.066 \\ CCA+ & KSETE & \textless{}0.001 & 0.001 \\ CCA+ & GMOTDP & \textless{}0.001 & \textless{}0.001 \\ KCCA+ & KSETE & 0.238 & 1.000 \\ KCCA+ & GMOTDP & \textless{}0.001 & 0.001 \\ KSETE & GMOTDP & 0.006 & 0.064 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Kruskal-Wallis H and Holm-Bonferroni correction. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Source** & **Target** & **TCA+** & **CCA+** & **KCCA+** & **KSETE** & **GMOTDP** \\ \hline \multirow{3}{*}{PC3} & AR3 & 0.5835 & 0.6618 & 0.7090 & 0.7129 & 0.7800 \\  & AR4 & 0.5796 & 0.6208 & 0.6790 & 0.6924 & 0.7630 \\  & AR5 & 0.5909 & 0.6092 & 0.5909 & 0.7334 & 0.8018 \\ \hline \multirow{3}{*}{PC4} & AR3 & 0.5998 & 0.6196 & 0.7049 & 0.6882 & 0.7951 \\  & AR4 & 0.5043 & 0.6167 & 0.6898 & 0.6764 & 0.7734 \\  & AR5 & 0.5797 & 0.5912 & 0.6008 & 0.7369 & 0.7823 \\ \hline \multirow{3}{*}{MW1} & AR3 & 0.5687 & 0.6366 & 0.6144 & 0.6939 & 0.7796 \\  & AR4 & 0.5454 & 0.6466 & 0.6544 & 0.7206 & 0.8148 \\ \cline{1-1}  & AR5 & 0.6891 & 0.6723 & 0.6009 & 0.7341 & 0.7622 \\ \hline \multicolumn{3}{c}{mean} & 0.5823 & 0.6305 & 0.6493 & 0.7099 & 0.7836 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Mean G-mean results for source and target projects with common features using different methods.