esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Extracellular Vesicles
### Content and Biogenesis
The first step is the enrolling of ESCRT-0 subunits, which select cargo in a ubiquitin-dependent manner; ESCRT-I and ESCRT-II are involved in budding endosomal membrane formation, and ESCRT-III is deputed to ILV scission and recycling of the ESCRT machinery [89]. Tumor susceptibility gene 101 (TSG101) and ALIX are two important members of the ESCRT complex discovered by [PERSON] et al. in a proteomic analysis of mouse dendritic cell-derived exosomes [90]. Exosomes can also be formed through an ESCRT-independent mechanism. The sphingolipid ceramide plays a role by creating a spontaneous negative curvature in the membrane leaflets of MVEs to shape ILVs [91]. The formation and the sorting of ILV cargo can also be regulated by the syndecan-syntenin-ALIX pathway. Indeed, syndecan heparan sulphate proteoglycans are connected to their cytoplasmic adaptor syntenin, which interacts with ALIX hence promoting endosomal membrane budding [92]. Finally, exosomal surface proteins belonging to the tetraspanine family, such as CD63 CD81, CD82 and CD9 or heat shock protein (Hsp70), may control ESCRT-independent exosomes biogenesis [93; 94]. Intracellular vesicular trafficking, fusion with the plasma membrane, and consequently, exosome secretion are driven by Rab GTPases proteins [95]. Mechanisms regulating the sorting of nucleic acids in exosomes are still not known. In summary, microvesicles bud directly from the plasma membrane, while exosomes are firstly incorporated into the endosomal vesicles and then released into the extracellular milieu by fusion of MVEs with the plasma membrane. Once released, EVs are absorbed by neighboring cells by endocytosis, phagocytosis, pinocytosis, or fusion of membranes. Ligands on the extracellular vesicle membrane can also bind to a receptor on target cells to thereby induce an intracellular signal [96].
# Network Traffic Prediction Incorporating Prior Knowledge for an Intelligent Network
## 4 Predictive Models
### Flow Feature Extraction Module  
Traffic feature extraction is one of the key steps in network traffic prediction. A traditional neural network consists of a three-layer structure with input, hidden, and output layers and, although it can extract features and map from features to values, it faces the problem of requiring a large number of parameters. In this paper, we use a multi-layer feed-forward neural network structure (i.e., a CNN), which adds a feature learning part to the traditional neural network and selects features by means of the network itself. Convolutional neural networks are characterized by weight sharing, translation invariance, and local connectivity, allowing for a higher level and more abstract representation of the raw data. By removing noise and unstable components from the data, the local features of the network traffic can be effectively captured. Although CNNs have achieved great success in the field of image processing (i.e., considering two-dimensional data), one-dimensional (1D) data are suitable for processing time-series, such as time series for speech recognition [24], stock prediction [25], etc.  
Network traffic size is a one-dimensional sequence that varies over time and can be represented using a \(1\times N\) matrix. One-dimensional convolutional neural networks have a convolutional kernel size of \(1\times K\) and use M convolutional kernels to compute sliding over \(1\times N\) data, mapping the original traffic data \(1\times N\) to a high-dimensional feature space (\(M\times N\)) and providing a good feature embedding for subsequent capture of temporal features. Traditional neural networks use spatial relativity to reduce the number of parameters and solve the problem of a large number of neural network parameters. Figure 3 shows the 1D convolution process for the hth feature of the lth layer and the  
If \(s\) is the number of convolution kernels, the lth feature of the \(h\)th layer can be written as:  
\[x_{h}^{l}=(x_{h1}^{l},\dots,x_{hm}^{l},\dots,x_{hs}^{l}) \tag{5}\]
## 2 Materials and Methods
### SARIMA Model  
For the SARIMA model, the univariate chl-a data was used, while maintaining the partitions in the training and test dataset. To test stationarity, the Augmented Dickey-Fuller test (ADF) was applied indicating significant stationarity (\(p<0.05\)) in the train and test datasets. To fill the model (p, d, q) \(\times\) (P, D, Q)\({}_{365}\), where 365 represents the seasonality, the best autoregressive (p, P) and moving average (q, Q) parameters were selected using an iterative method in the train dataset. The parameters ranged from 0 to 4 in the nonseasonal parameters (p, q) and 0 to 2 in the seasonal parameters (P, Q), selecting the combination with lowest Akaike information criterion (AIC). The difference order parameters d and D were 0, due to the stationarity results of the ADF test. The best parameters selected using the training dataset were (4, 0, 1) \(\times\) (2, 0, 1)\({}_{365}\), and this SARIMA model was used to fit the test dataset.
# Security and Privacy in Physical-Digital Environments: Trends and Opportunities
## 4 Challenges and Solutions
### Socialization  
The Metaverse represents a virtual world where individuals can interact without being bound by traditional geographic limitations. The underlying infrastructure relies on internet servers to facilitate connectivity, exposing it to network-related security and privacy concerns analogous to those in social networks. As such, the Metaverse's potential to enable seamless communication across vast distances is under constant threat from vulnerabilities inherent to modern networked systems.  
The Metaverse, being an interactive virtual space, is operated through user inputs. However, it presents an opportunity for hackers to engage in unauthorized access to sensitive data through injection attacks (untrusted inputs or unauthorized code sent from attackers and interpreted as valid commands or queries) [39].  
The majority of cyber-attacks are the result of security vulnerabilities, often stemming from a lack of input validation. To mitigate the risks associated with these attacks, employing the SQL defense model [40] is a recommended best practice. The SQL defense model involves a three-step process to validate input information. Firstly, the server side verifies the legitimacy of the IP address, followed by testing input values for format, length, type, and range. Lastly, the server verifies the user's privileges, and in the event that the user's access permissions exceed the allowable limit, the user is immediately blocked and the system administrator is notified.  
One prevalent type of cyber attack is the man-in-the-middle attack [41], in which a malicious actor eavesdrops on or interferes with the communication between a user and a server, with the ultimate goal of intercepting confidential data. To mitigate the risk of such attacks, it is recommended to invest in authentication algorithms that can be applied to safeguard data integrity. The effectiveness of these algorithms stems from their ability to verify the identity of both parties involved in a communication session, thereby ensuring that the data exchanged between them are authentic and secure.  
In the Metaverse, where MR technologies are increasingly used, socialization relies on real-time data exchanges that enable users to interact within virtual spaces. In MR environments, socialization can include shared virtual experiences overlaid on physical spaces, creating additional security risks. Because MR devices often capture audio, video, and environmental data, they introduce vulnerabilities to eavesdropping attacks and man-in-the-middle attacks. Attackers can intercept or manipulate MR data streams, potentially altering the shared experience or extracting sensitive data.  
MR platforms should, therefore, enforce end-to-end encryption and adopt secure session management techniques to protect data as they flow between users. Additionally, regular security updates and vulnerability assessments for MR applications can help protect against emerging threats [25].
## 4 Discussion
### Satellite Position Error  
In the derivation of Equation (7), we assumed that the satellite position error was completely removed from the double-differenced measurement. However, it is not always true, and the contribution of the satellite position error to the relative position vector estimation depends on the baseline length and the satellite altitude. As a rule of sum, the contribution of the satellite position error, \(\delta b\), can be approximated by the following equation:  
\[\delta b=\frac{b}{h}\delta x^{S}, \tag{23}\]  
where \(b\) is the baseline length between the lander and the rover, \(h\) is the satellite altitude, and \(\delta x^{S}\) is the satellite position error.  
In the demonstration experiment of MDPO, the baseline length is about 5 km. Since the typical GPS satellite altitude is 20,200 km and the broadcast ephemeris error is about 1 m, the contribution of the satellite position error is about 0.2 mm. It is much less than the 2 drms relative position errors shown in Table 4 and negligible.  
On the other hand, the altitude of the lunar orbiting satellites is assumed to be 300 km. For the same baseline and satellite position error of the demonstration experiment, the resulting \(\delta b\) becomes 16 cm. If we would need to limit the baseline to 10 km and keep the magnitude of \(\delta b\) less than a few meters, it is required to have a lunar system that can provide a satellite ephemeris accuracy of better than 100 m. It is still challenging but not completely unreasonable. We are currently working on a detailed performance evaluation of our lunar satellite orbit determination system using an \(X\)-band range and range rate transponder.
# Energy-Efficient Wireless Communication Strategy for Precision Agriculture Irrigation Control
## 2 Precision Irrigation Control System  
An automated precision irrigation process can be implemented using soil moisture readings in the crop-root zone to feedback the system; meanwhile, weather conditions represent the system disturbance measured to compensate their effects in advance [9]. The soil moisture indicates the percentage water content available in a specific soil volume sample and is often referred to as the volumetric water content (VWC). The reference evapotranspiration (ETO) represents the water loss caused by weather conditions for a specific reference crop (grass). A crop coefficient \(K_{c}\) is required to convert this reference value into a crop specific parameter. Solar radiation, wind speed, air temperature, and relative air humidity measurements are required to calculate this parameter, according to the FAO Penman-Monteith equation [38]. The evapotranspiration is generally expressed in millimeters (mm) of water lost from a crop surfaced per unit of time (days).
# Single vs. Multi-Label: The Issues, Challenges and Insights of Contemporary Classification Schemes
## 2 Overview of State-of-the-Art Approaches
### Critical Analysis of Contemporary Approaches Based on Evaluation Criteria
#### 2.4.2 Analysis of Content-Based Approaches (Multi-Label Classification)  
The state-of-the-art approaches which exploit the content of documents and have performed multi-label classification are shown in Table 2. These approaches have predicted one or more than one classes from the multiple classes. However, there are very few state-of-the-art approaches which perform multi-label classification. For multi-label classification, [PERSON] [36] presented an approach which utilizes an ACM dataset and ACM classification system which contains eleven classes at its root level. They have applied different approaches (algorithms) for the multi-label classification and achieved accuracy up to 0.88. Similarly, [PERSON] [37] also performed multi-label classification and applied an algorithm on different datasets such as WIPO-alpha, 20-Newsgroups, Enzyme, etc., and achieved accuracy up to 0.94 and precision up to 0.84. [PERSON] and [PERSON] [38] also presented a multi-label classification approach which uses 45,000 features to classify documents and classifies accurately up to 0.61. As already mentioned, multi-label classification is relatively more vulnerable to misclassification compared to the binary classification.
## 2 Hybrid Real-Time Scheduler in the MIROS
### Scheduling Background of the Wireless Sensor Network Operating Systems (WSN OSes)  
The scheduling models can be classified into two kinds: the multithreaded scheduling and the event-driven scheduling. Multithreaded scheduling is commonly used on the general-purpose OSes, such as Linux and uCOS. Different tasks can be executed concurrently by the threads, and the preemption among the threads can be achieved by the context switch. With the preemption support, the real-time (RT) reaction can be achieved. However, each thread in the preemptive multithreaded OS should have an independent stack. Thus, the data memory usage of the preemptive multithreaded OS is high, making the preemptive multithreaded OS unsuitable for the high resource-constrained WSN nodes, e.g., the MicaZ node which has only 4 KB RAM. To address this challenge, the event-driven scheduling is developed. Different from the preemptive multithreaded OS, each task in the event-drivenOS should run to completion before the execution of the next task. As a result, only one stack is needed in the event-driven system and a considerable amount of data memory can be saved if compared to the preemptive multithreaded system. In article [9], the results show that the event-driven OS can achieve 30-times improvement in data memory consumption over the general-purpose multithreaded OS. This is significant for the resource-constrained WSN platforms. However, the deadline of real-time tasks cannot be guaranteed in the event-driven OS due to the non-support of the preemption. As a result, the event-driven OS is not suitable to be used for real-time WSN applications.  
Two scheduling models have different strong and weak points. The design and implementation of a new OS scheduler that cannot only support the real-time scheduling, but also consume less data memory will be a key research challenge for the WSN OS.
# Performance Comparison of ANFIS Models by Input Space Partitioning Methods
## 2 Adaptive Neuro-Fuzzy Inference System (ANFIS)
### Learning Method of Adaptive Neuro-Fuzzy Inference System (ANFIS)  
The ANFIS model uses a hybrid learning method [45] that combines the least square method and a back propagation algorithm for learning. The hybrid learning method updates the conditional and conclusion parameters of the rule so that the error of the output can be minimized using given input and output data. The conditional parameters have a nonlinear structure, and the conclusion parameters have a linear structure. This learning method consists of forward learning and reverse learning. The forward learning process uses LSE to fix the conditional parameters and optimize the conclusion parameters. When the optimal conclusion parameter is obtained in the forward learning process, the reverse learning process is performed. The backward learning process uses the gradient descent method to optimize the parameters placed in the conditional part of the rule that defines the membership function. The above process is repeated until the error threshold of the ANFIS model is reached or when the number of repetitions specified by the user is reached. Figure 2 shows the learning process of the ANFIS model.  
Figure 1: Structure of an adaptive neuro-fuzzy inference system (ANFIS).
# Hierarchical Fusion of Machine Learning Algorithms in Indoor Positioning and Localization
###### Abstract  
Wi-Fi-based indoor positioning offers significant opportunities for numerous applications. Examining the Wi-Fi positioning systems, it was observed that hundreds of variables were used even when variable reduction was applied. This reveals a structure that is difficult to repeat and is far from producing a common solution for real-life applications. It aims to create a common and standardized dataset for indoor positioning and localization and present a system that can perform estimations using this dataset. To that end, machine learning (ML) methods are compared and the results of successful methods with hierarchical inclusion are then investigated. Further, new features are generated according to the measurement point obtained from the dataset. Subsequently, learning models are selected according to the performance metrics for the estimation of location and position. These learning models are then fused hierarchically using deductive reasoning. Using the proposed method, estimation of location and position has proved to be more successful by using fewer variables than the current studies. This paper, thus, identifies a lack of applicability present in the research community and solves it using the proposed method. It suggests that the proposed method results in a significant improvement for the estimation of floor and longitude.  
hierarchical data; fingerprinting; indoor positioning; information fusion; localization; machine learning; Wi-Fi +
Footnote †: journal: Medical Imaging
# Authentication Based on Non-Interactive Zero-Knowledge Proofs for the Internet of Things
## 8 Comparative Analysis
### ZKP Schemes  
We have analyzed several lightweight authentication schemes applied to the Internet of Things. For instance, [39] proposes a zero-knowledge proof authentication algorithm based on isomorphic graphs and describes its evaluation and implementation. The proposed mechanism allows authentication with varying confidence and security levels. Such a work describes an implementation on conventional computers (with different configurations), so that the starting conditions are more  
Figure 7: Segment processing time trend.  
powerful than when using mobile devices, as in this paper, mainly because of the efficiency of the used programming language. On the one hand, the scheme proposed in this paper uses Java as a programming language, so that the implementation is as cross-platform as possible and, thus, can be applied to many current mobile devices. On the other hand, the compared scheme uses random graphs of 41 nodes. In order to allow the comparison, the scheme proposed in this paper has been also implemented with graphs of 41 nodes. The comparison results are shown in Table 2, where the four different hardware configurations described in the paper are shown. Another consideration is the fact that the compared scheme uses an interactive ZKP with six exchanges of messages, while the proposal of this paper is based on an NIZKP in which only one single message is necessary.  
From Table 2, it can be concluded that the scheme that has been designed here is computationally faster considering the average results and characteristics of both implementations. On the other hand, the compared scheme uses fewer memory bytes. However, the relationship between memory and time is better in the scheme proposed here than in the others. This is due to the fact that the difference in memory space of a few kilobytes is acceptable for today's mobile devices, both when being sent with wireless technologies and when being stored and processed.
## 2 USV System Structure
### Navigation Module  
The navigation module received data from sensors such as GPS(Global Positioning System), INS(Inertial Navigation System), compass, forward looking sonar, radar, and LiDAR(Light Detection and Ranging), etc. After getting the sensor data, the navigation module analyzed the USV's position, speed, heading, and obstacle information, and gave the USV's next speed and heading combined with the mission requirements. If obstacles existed, the speed and heading had to be adjusted through the corresponding collision avoidance algorithm. The architecture of the navigation module is shown in Figure 4.  
Figure 4: Navigation module.
## 4 Case Study and Discussion
### Case Study
#### 4.2.2 Case Results  
From the power system operator's perspective, power shortages might be a major concern in operations since the shortage has to be supplemented by operating additional power resources. However, surplus power can be easily handled by curtailing wind power generation. Thus, the operator would pay attention to discharging patterns when power shortages occur. Therefore, this study focused on representing the results, including when the power exceeded the lower variation criteria and the discharging power. The average discharging power in off-peak and peak periods was estimated to evaluate the effect of applying the REC multiplier, as depicted in Table 5. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Case Number**} & **Status of REC Multiplier** & \multirow{2}{*}{**Magnitude of REC Multiplier**} \\  & **in Off-Peak Period** & \\ \hline
1 & X & - \\
2 & O & 1 \\
2-1 & O & 1.4 \\
2-2 & O & 3 \\
2-3 & O & 4.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of the case setup. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Month**} & **Average System Marginal Price** & **Average REC Price** \\  & **[Won/kW-5 min]** & **[Won/kW-5 min]** \\ \hline
1 & 8.85 & 20.51 \\
2 & 10.6 & 17.72 \\
3 & 11.06 & 17.49 \\
4 & 10.11 & 17.28 \\
5 & 10 & 19.07 \\
6 & 10.39 & 17.38 \\
7 & 10.12 & 17.92 \\
8 & 9.39 & 17.88 \\
9 & 9 & 19 \\
10 & 8.87 & 16.44 \\
11 & 10.79 & 18.05 \\
12 & 10.77 & 14.59 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Monthly average system marginal price (SMP) and REC price data. ESS operations under current policies could be modeled by case 1. As shown in Table 5, the ESS did not discharge in off-peak periods, while it discharged in peak periods. As expected, results of cases 1 and 2 are same. The results of the discharging power are first observed in case 2-1. In addition, the average discharging power during the peak period in case 2-1 was slightly higher than that in cases 1 and 2. This discharging power was presumed to increase due to altered ESS operations in the off-peak period. The average discharging power in the off-peak period in case 2-2 was higher than in case 2-1 due to an increased REC multiplier. However, the average discharging power in the peak period in case 2-2 was lower than that in case 2-1. These results are presumed to be due to the increase in discharging power during the off-peak period. Case 2-3 exhibited different trends. The average discharging power during the off-peak period in case 2-3 was slightly lower than that in Case 2-2. This result was compared with the increasing trend that existed in the discharging power during the off-peak period in cases 2-1 and 2-2. The average discharging power in the peak period in case 2-3 was lower than in the other cases. The amount of power that exceeded the lower variation criteria is presented in Table 6. The effect of the REC multiplier on the power shortage is examined based on these case results.
## 2 Calculation Model and Solution Method
### Establishment and Solution of the Difference Equation
#### 2.2.4 Solving Steps  
The first step for solving the system is to divide the pile into \(n\) segments, according to the geometric dimensions of the pile foundation and the distribution conditions of each of the soil layers. The second step, is to preset the initial value of the foundation resistance coefficient, \(c_{z0}\), of the soil at the node of the pile element. In the third step, the finite difference method is used to solve the displacement of each node, \(y_{i}\), and the fourth step is to determine the pile side soil resistance, \(p_{i}\), from \(y_{i}\), according to the \(p\)-\(y\) curve of the different soil layers. Next, step 5 is to obtain a new \(c_{zi}\) from \(c_{zi}=p_{i}/y_{i}\). Finally, the sixth step is to use the finite difference method to calculate the new node displacements according to the new \(c_{zi}\). If the displacement difference of each node obtained by two iterations is within the error limit \(\varepsilon\), then the process is stopped, otherwise steps 4 to 6 are repeated until the error limit is reached.