esa-sceva/satcom-chunk-collection huggingface dataset:
# Survey of Blockchain-Based Applications for IoT
## 1 Introduction  
The expanding IoT is affecting multiple industries, with the total number of connected devices anticipated to nearly double, growing from 15.9 billion in 2023 to above 32.1 billion by 2030 [1]. As these networks grow, they increasingly demand frameworks to support security, scalability, interoperability, and data integrity. Traditional centralized systems face challenges in meeting these needs [1], particularly in environments with limited resource devices with no central authority. As a distributed digital ledger [2], blockchain technology offers a tamper-resistant and scalable solution by leveraging cryptographic hashes and decentralized consensus mechanisms to share data securely between IoT systems [3]. Initially developed to power Bitcoin [4], blockchain applications have since expanded into numerous sectors, addressing critical needs, such as transaction verification and immutable record storage [2]. With features such as decentralization, immutability, and robust data protection, blockchains provide a secure, transparent, and trustworthy framework that is well-suited for IoT applications [5].  
Incorporating blockchains into IoT ecosystems enables a decentralized trust model that supports secure peer-to-peer communication among devices without intermediaries. Blockchains' immutable record-keeping and smart contract [6] functionalities further strengthen IoT automation and security by ensuring that data remains unaltered once recorded. Integrating blockchains with the IoT boosts transparency and accountability through established audit trails and verifiable transactions, which are critical for supply chain management, smart cities, healthcare, and industrial automation.  
This paper offers a comprehensive look at recent research on blockchain applications in the IoT. It explores the diverse blockchain architectures and protocols used in IoT settings and draws attention to significant trends and potential areas for further study. By analyzing the merging of these transformative technologies, this survey provides a perspective on how blockchains can enhance IoT networks, making them more secure, scalable, and transparent. Furthermore, it highlights the role of smart contracts and oracles in automating processes, reducing operational costs, and enabling trustless interaction. The survey also discusses the importance of layer 2 solutions, tokenization, and decentralized data sharing in addressing scalability challenges and fostering data sharing and real-time decision-making across IoT ecosystems.
## 4 Localized Query Loss Discovery with Distributed Query Resolution
### Collective Query Loss Detection  
Let \(P\) denote the probability of a query loss at an object sensor node in the query dissemination. Then, we know that the successful query delivery probability at a sensor node is \(S_{1}=1-P\). For \(n\) detectors of an object node, the successful detection of query loss requires at least one of the detectors to successfully receive the query packet. Since the probability that all nodes lose the query packet is \(P^{n}\), the probability of successful query delivery to at least one of the detectors is \(S_{2}=1-P^{n}\). Suppose that a detector that successfully received a query packet can successfully detect a query loss of its object node. Then, \(S_{1}\) is the probability of query loss detection by a single detector for its object node to lose the query packet. Furthermore, \(S_{2}\) is the probability of query loss detection by a cluster of detectors for its object node that  
Figure 4: State transition of a detector node.  
Figure 3: Query to address resolution table at loss detectors.  
loses the query packet. Hence, the ratio of successful detection of query loss at an object node between the single detector approach and the collective detector based approach is shown in Equation (1).  
\[R_{success}=\frac{S_{2}}{S_{1}}=\frac{1-P^{n}}{1-P} \tag{1}\]  
Figure 5 illustrates the impact of utilizing loss detectors on the successful detection of query losses by varying the probability of a query loss. The utilization of collective detectors keeps a successful delivery probability that is at least equal to or higher than that of single query detector. For example, in case that the number of loss detectors of a sensor node is \(n=12\), the success probability of the collective detector approach is maintained above 90 % when the loss probability \(p<0.8\), and the ratio is up to 4.5 times to that without utilizing the collective loss detectors.
## 4 Results
### Results of Individual Studies and Synthesis
#### 4.1.2 Routing and Clustering Approaches
This protocol is particularly advantageous for long-duration IoT applications, such as environmental and industrial monitoring, where consistent and prolonged network performance is essential.
# Time-Frequency Domain Seismic Signal Denoising Based on Generative Adversarial Networks
## 3 Results  
We used the STanford EArthquake Dataset (STEAD) [38] as the dataset. STEAD is a high-quality, large-scale, and global dataset of local earthquake and non-earthquake signals recorded by seismic instruments. In this paper, 47,335 signals with an SNR greater than 50 dB were randomly selected from the STEAD as the clean signal set, and 100,000 noise signals were randomly selected as the noise set. These were then divided into training, validation, and test sets in a 3:1:1 ratio. During the training process, the signal was randomly selected from the training clean signal set and the training noise set, respectively, and added together to obtain a noisy signal. The validation process is similar to the training process. For the synthetic test set, we generate a set of random numbers, where each random number represents the index of the corresponding noise signal from the test noise set for each clean signal in the test clean signal set. During the testing process, these two signals are added together to obtain a noisy signal. For the field test set, 20,000 signals with SNR lower than 20 dB were randomly selected from the STEAD as the test set. The SNR formula used in this paper is different from the one used in STEAD, which is shown in Equation (9). We use the same formula as the one used in DeepDenoiser.  
\[\text{SNR}=10\log_{10}\frac{\left\|S\right\|^{2}}{\left\|N\right\|^{2}} \tag{9}\]  
We compare our proposed method with DeepDenoiser and ARDU on synthetic and field seismic signals. The performance is assessed using metrics of signal-to-noise ratio (SNR), correlation coefficient, and mean absolute error (MAE). The corresponding formulas defined are as follows:  
\[\begin{split}\text{SNR}=10\log_{10}\left(\frac{\sigma_{x}}{ \sigma_{n}}\right)\\ r(X,\hat{X})=\frac{Cov(X,\hat{X})}{\sqrt{Var[X]\cdot Var[\hat{X}]}}\\ \text{MAE}=\frac{1}{n}\sum\limits_{i=1}^{n}\left|\hat{X}(i)-X(i) \right|\end{split} \tag{10}\]  
In Equation (10), \(\sigma_{s}\) and \(\sigma_{n}\) represent the standard deviations of the signal before and after the arrival of the P-wave. _Cov_ represents covariance, and \(Var\) represents variance.
###### Abstract  
Radiocesium monitoring in sediments and river water has been conducted along five rivers in Minami-Soma City during 2012-2016 to clarify the temporal changes of radiocesium contamination in these rivers. Sampling has been performed annually under normal flow conditions. Sediment and river water samples were collected from four or five sampling sites along each river. Gamma-ray measurements of sediments were performed using a low-background Ge detector and unfiltered river water was utilized to determine radiocesium concentration using a well-type Ge detector. The \({}^{137}\)Cs concentration in sediments was highest at upstream sites and slowly decreased to downstream sites for all rivers reflecting the high radioactive contamination in the upstream area. Temporal decrease of the \({}^{137}\)Cs concentration was observed in sediments and river water for each river. The effective half-lives were 1.3-2.1 y for sediments, and 0.9-2.1 y for river water from rivers with upstream dams. On the undammed river, the effective half-lives were 4.7 y and 3.7 y for sediment and river water, respectively. Much longer effective-half-lives might reflect the direct transfer of radiocesium from forests and plains to the river. The \({}^{137}\)Cs concentration in riverbed was low in downstream areas, however, accumulation of \({}^{137}\)Cs over the floodplain was observed. Rapid decrease of \({}^{137}\)Cs contamination through rivers will put residents at ease, but high accumulation of radiocesium over floodplains should be noted for future river decontamination.  
long-term monitoring; radiocesium; FDNPP; Minami-Soma City; sediments; river water; effective half-life +
Footnote †: journal: Applied Soft. 2018, 8, 1319; doi:10.3390/app8081319
## 3 Tracing Method from CVE-ID to CAPEC-ID  
The proposed approach involves four steps. First, a corpus of CVE descriptions and 546 CAPEC-ID documents are generated. Figures 1-3 show examples using CVE-2020-10108 as the input data. Second, a document embedding is created using a similarity algorithm. The document measure in Figure 1 created 547 vectors, while the section measure in Figures 2 and 3 created 2692 vectors. Third, the CVE-ID vector and all CAPEC vectors are used to calculate the cosine similarity (Equation (1)). Finally, the CAPEC documents are sorted by the similarity score and N CAPEC-IDs are selected. Then, candidates for the associated CAPEC-IDs are obtained from CVE-IDs.  
\[\cos\left(\overrightarrow{p}\cdot\overrightarrow{q}\right)=\frac{p_{1}q_{1}+p_ {2}q_{2}+\ldots+p_{n}q_{n}}{\sqrt{{p_{1}}^{2}+{p_{2}}^{2}+\ldots+{p_{n}}^{2} }\sqrt{{q_{1}}^{2}+{q_{2}}^{2}+\ldots+{q_{n}}^{2}}}=\frac{\overrightarrow{p} \cdot\overrightarrow{q}}{\left|\overrightarrow{p}\right|\left|\overrightarrow{ q}\right|} \tag{1}\]Three similarity measures are proposed to trace CAPEC from CVE. The first one measures the similarity of all sections as a single document (Figure 1). The second measures the similarity for each section (Figure 2). The third measures the similarity for each section and then calculates the average of the similarity for all sections per CAPEC-ID (Figure 3).  
In addition, three algorithms are considered to create document embeddings: TF-IDF, USE, and SBERT. Below is an explanation for finding the associated CAPEC-ID using CVE-2020-10108 as the input data. All algorithms are demonstrated using the flow of tracing in combination with the document patterns.  
Figure 1: Overview of the proposed approach using the entire document.  
Figure 2: Overview of the proposed approach using all sections.
## 3 Proposed Method
### Ensuring Authorization Security
#### 3.2.1 Preprocessing  
To transform static-authorization logics and dynamic mutual role constraints into hard clauses, we describe the transforming rules as follows:  
\begin{table}
\begin{tabular}{c l} \hline \multicolumn{1}{c}{RSSOD} & \multicolumn{1}{c}{**SMERs**} \\ \hline \(\epsilon_{1}\)=rssod<\(\{r\)r\({}_{1}\),\(r\)\({}_{2}\),\(r\)\({}_{3}\),\(r\)\({}_{4}\),\(r\)\({}_{5}\)],\(2\)\(>\) & \(C_{1}\)=[smer<\(\{r\)r\({}_{1}\),\(r\)\({}_{2}\),\(r\)\({}_{3}\),\(r\)\({}_{4}\),\(r\)  
\(\forall p\in P,\exists s\in S,\forall r\in R,:p\in P_{lb}\Rightarrow p\in\cup_{ \mathit{rerolles\_session(s)}}\mathit{perms\_session}(r).\)  
Similarly, permissions not in \(P_{ub}\) must not be satisfied or available in a single session, which can be formalized as follows:  
\(\forall p\in P,\exists s\in S,\forall r\in R,:p\in(P\backslash P_{ub}) \Rightarrow p\
otin\cup_{\mathit{rerolles\_session(s)}}\mathit{perms\_session}(r).\)  
Different cases can be easily transformed into the partial MAX-SAT. Essentially, the partial MAX-SAT problem is a special extension of the MAX-SAT problem, whereby some clauses are relaxable and others are hard clauses. It aims to satisfy all hard clauses as well as a maximum number of soft ones. To translate the authorization-query problem to a partial MAX-SAT instance, all permissions in \(P_{lb}\) should be inserted into \(HC\), and all permissions in \((P_{ub}\backslash P_{lb})\) should be inserted into \(SC\). Next, to satisfy the \(max\) match, any permission in \((P_{ub}\backslash P_{lb})\) is initially set to be true, as only permission variables can be used in the permissions request and the partial MAX-SAT is to satisfy the largest number of soft clauses; to satisfy the \(min\) match, on the other hand, any permission in \((P_{ub}\backslash P_{lb})\) is initially set to be false. Thus, hard and soft clauses are updated in Algorithm 5.  
```
0: the UAQ \(<\)s, \(P_{lb}\), \(P_{ub}\), match\(>\), preprocessed results \(HC\), \(SC\)
0: updated results \(HC\), \(SC\)
1:for each \(p\) in \(P_{lb}\)do
2:\(HC\)=\(HC\cup\{\overline{p}\}\);
3:endfor
4:for each \(p\) in \(P\backslash P_{ub}\)do
5:\(HC\)=\(HC\cup\{\
eg\overline{p}\}\);
6:endfor
7:for each \(p\) in \(P_{ub}\backslash P_{lb}\)do
8:if match = minthen
9:\(SC\)=\(SC\cup\{\
eg\overline{p}\}\);
10:elseif match = maxthen
11:\(SC\)=\(SC\cup\{\overline{p}\}\);
12:else
13:\(SC\)=\(SC\cup\Phi\);
14:endif
15:endfor
```  
**Algorithm 5** Updating clauses.  
In Algorithm 5, the two cases are reflected in lines 1-6. If permission \(p\) is in \(P_{lb}\), we represent \(p\) with literal \(\vec{p}\) and insert it into \(HC\). If permission \(p\) is not in \(P_{ub}\), we insert literal \(\
eg\vec{p}\) into \(HC\). If the requested permissions are not in \(P_{lb}\), but in \(P_{ub}\), lines 7-15 transform them into soft clauses according to the different matches. We insert literal \(\
eg\vec{p}\) or \(\vec{p}\) into \(SC\) when the match objective equals to \(min\) or \(max\), and we do not update \(SC\) when the match objective equals to \(exact\).
# Point of Interest Recognition and Tracking in Aerial Video during Live Cycling Broadcasts
## 2 Related Work  
While no directly related research was found that addresses point of interest recognition and tracking during live sports broadcasts, there are several related applications that perform landmark recognition to reduce manual work or enhance user engagement. For example, software services exist that process video footage to generate metadata for media content, thereby improving searchability and facilitating the retrieval of appropriate clips for storytelling purposes [3]. Additionally, smartphone applications designed for tourists can recognize points of interest in a city and provide relevant background information [4; 5]. To provide a broader perspective on existing literature, an overview is separately given of the relevant methods proposed for the two main components of our work: landmark recognition and object tracking.
# Deep Learning Methods in Image Matting: A Survey
## 6 Applications of Image Matting  
As mentioned earlier, image matting is a widely used image processing technique that has numerous applications in various scenarios. It involves image editing, medical imaging, cloud detection, and other related fields. In this section, we discuss the application scenarios where image matting plays a significant role as well as the fundamental principles behind their applications.  
**Image Editing:** Image matting is a versatile tool for fine-tuning and manipulating images to achieve various desired effects in image editing. Foreground separation is a key application of image matting, which enables image composition and finds use in diverse fields such as advertising design and video production [66; 67]. Additionally, image matting is useful in image inpainting [68], which is a technique for repairing damaged or missing parts of an image. Beyond these, image matting can also be utilized for background replacement and object movement; with the former, the background of an image can be replaced with another image or color, while the latter allows for the movement of the foreground object to another position. These applications can be used to achieve a range of visual effects for the image.  
**Medical Imaging:** In the field of medical imaging, image matting technology has found applications in the segmentation and analysis of medical images. For example, [PERSON] et al. [69] introduced alpha mattes of image matting into medical imaging, which better described lesion details and addressed the blurring problem associated with binary masks. Moreover, image matting can also be utilized for medical image segmentation and disease detection [70; 71]; the former can be used to separate various structures or tissues (such as lungs and bones) in medical images, assisting in analysis and diagnosis, while the latter can detect diseased tissues in medical images and help to locate lesion locations.  
**Cloud Detection**: The utilization of image matting technology can be extended to the domain of cloud detection [72; 73; 74]. Traditional techniques encounter challenges when attempting to accurately segment clouds in remote sensing images due to their uneven brightness and irregular shapes. However, image matting offers a more superior performance by separating the foreground and background of clouds, resulting in improved accuracy and efficiency in cloud detection.  
Aside from the mentioned applications, image matting is widely applicable in various other domains. For instance, in game development, this technology can facilitate the free movement and operation of game characters by separating them from the background. In intelligent transportation systems, image matting allows for the precise detection and tracking of vehicles or pedestrians by separating them from the background. Additionally, image matting is extensively utilized in multi-modality, automatic driving, and 3D applications.
# An Energy Efficient Routing Approach for IoT Enabled Underwater WSNs in Smart Cities
## 5 Conclusions  
In this study, the number of most recently used smart underwater routing protocols are analyzed. The terrestrial (ACH)\({}^{2}\) routing protocol is integrated with a smart underwater routing scheme. Due to significant advancement in smart underwater development, the sensor network has gained a lot of researchers' interest, but still, there are various situations where an optimal solution is required to be explored. The results of underwater implementation of (ACH)\({}^{2}\) varies according to the acoustic modem type. In the proposed scheme, sink nodes are deployed in three different ways. In the first case, the only single sink is placed at the water surface. In the second case, four sinks are arranged and positioned at an equal distance from one another at the water surface. While, in the third case, one of the sinks is placed at the water surface and the second sink is deployed inside the predefined \(3-D\) region. Moreover, all of the aforementioned cases are evaluated separately. Afterwards, the simulation results show that the maximum number of packets are favorably reached at the BS and less number of nodes died as compared to DBR and EEDBR. DEEC-(ACH)\({}^{2}\) performs best generally, while, in case 2, LEACH-(ACH)\({}^{2}\) performs best.  
In the future, we intend to enhance our proposed scheme by considering link status and signal to noise ratio before sending any packets to BS. We are also interested in using the mobile sinks in our future study to support smart cities' vision.  
The research conceptualization and methodology were done by [PERSON], O.A. and S.U. The simulations are performed by N.U., S.U., and A.M.K. The technical review and improvement were performed by [PERSON] and A.S. The overall technical support, guidance and project administration was done by B.H. Finally, responses to the queries of the reviewers were done by [PERSON] and M.A. All authors have read and agreed to the published version of the manuscript.  
This research work was supported by Zayed University RIF research fund Number R19046.  
The authors declare no conflict of interest.
## 3 Collaborative AUVs
### Collaborative AUVs Overview  
Nature of underwater environments makes the use of communication systems with high-frequency signals difficult. This due to the rapid attenuation that permits propagation only at very short distances. Acoustic signals have a better performance, but face many challenges such as signal interferences and small bandwidth, which results in the need for time synchronization methods and hence, produce a high latency in the system. Another option is a light-based system, which offers a higher bandwidth but at short/medium ranges. Blue/green light has better propagation in underwater than any other light; but, when the range for communication is increased, the power consumption, weight, and volume of the equipment required also increase.  
If the inter-vehicle communication system is good enough in terms of range, bandwidth and rate, range-only/single-beacon can be an effective method for target localization and collaborative navigation of teams of AUVs. Vision-based systems are also an option that has the potential to control AUV formations without the need of relying on inter-vehicle communications, but only if the environmental conditions are favorable for light propagation and sensing.  
Collaborative missions are quite difficult to implement in real conditions. Assembling a team of AUVs with the proper technology to overcome localization, navigation, and communication shortcomings results difficult for researchers who have to limit their proposals to numerical simulations. Most of the authors use MATLAB Simulink to perform their simulations and some tools such as the former SimMechanics (now called Simscape Multibody). Another simulation environment commonly used for underwater robotics is the UnderWater Simulator (UWSim) [149]. With those tools, researches are working in pushing the state-of-the-art in terms of control, localization, and navigation algorithms. Within them, machine learning algorithms are gaining quite an attention. They are being employed in different aspects such as navigation [150; 151], obstacle avoidance and multi-AUVs formation control.