esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Statistical Analysis  
The statistical processing and analysis of the data was carried out using the JAMOVI statistical package. Initially, a principal component analysis was applied together with logistic regression and normality analysis, with the objective of identifying variables with the potential to influence the modification of health bio-parameters. Next, comparisons of means were made to evaluate the existence of significant differences between the groups at specific times: before, during, and after the intervention. An analysis of covariance (ANCOVA) (linear model; time \(\times\) groups) was used to analyze the effects of the intervention on the results. Baseline variable results were included as covariantss as potential confounding factors. Statistical significance was set at \(p<0.05\) for all tests. The results are reported as means and standard deviation for each group. Additionally, the effect size was analyzed to provide a quantitative measure of the magnitude of the differences found.  
Correlation analysis was performed using [PERSON]'s coefficient. This value was contrasted with the following hypothesis (using a 95% confidence coefficient): H0 (null hypothesis), there is no correlation between the two variables; H1 (alternative hypothesis), there is correlation between the variables. The \(p\)-value was calculated and values below 0.05 were determined to indicate a correlation between the variables. The correlation values ranged from \(-1\) to \(1\), where negative values indicate an inverse correlation (when one variable increases, the other decreases) and positive values indicate a direct correlation (both variables increase or decrease). The closer the value to \(-1\) or \(1\), the stronger the correlation.
# An Audio Copy-Move Forgery Localization Model by CNN-Based Spectral Analysis
## 3 Experiments
### Localization of Audio Copy-Move Forgery
#### 3.3.3 The Influence of Forgery Duration on the Model  
For copy-move forgery detection, shorter forgery durations make forged segments easier to conceal, making them harder to detect and locate. Considering that under normal speech rates, a person can speak 3 to 5 words per second, to assess whether the proposed model's performance significantly fluctuates with changes in the duration of replicated segments, forgery durations were grouped and tested from 0.3 s to 1.5 s in increments of 0.1 s (0.3 s-0.4 s is recorded as 0.3 s). Figure 8 presents a line graph of detection accuracy, showing a slight decrease in model performance as the forgery duration shortens. However, overall, the model maintains suitable performance, achieving over 90% detection accuracy for copy-move forgery audio with replication durations exceeding 0.3 s. Further calculations on accurately detected data yield an average localization error of 0.033 s.
# PVPBC: Privacy and Verifiability Preserving E-Voting Based on Permissioned Blockchain
## 3 Core of the PVPBC Voting System
### System Component Overview  
Figure 1 illustrates the PVPBC voting system's components. Further explanation of how these components work will be provided in Section 4, but a brief overview is provided now in order to provide a working knowledge of the system.  
**Trusted third party (TTP):** an individual or organisation which ensures that the election is correctly held by making use of DL technologies and smart contract to verify eligible voters.  
**Front-end system:** a self-contained component which provides a verifiability functionality similar to the Selene protocol. A crucial difference from the Selene protocol is that the cryptographic functionality is provided by the front-end system, which includes key generation, signing, and decryption for voters. As a result, these functionalities do not have to be provided by the voters. This is managed independently of the EA. The front-end system also provides the authentication and voting services for voters. Specifically, the front-end system verifies voter-authentication requests and allows eligible voters to access the ballot and to cast their votes.  
The front-end system is composed of several components. These components collectively provide secure and verifiable voting.  
* **Voter-key management:** This component manages voter keys and their application. This is run, not by the EA, but by an independent trusted party.
* **Tellers:** generation and management of the election threshold key and the cryptographic manipulations required by the Selene protocol is performed by a set of tellers [30].
* **Web bulletin board (WBB):** To distribute trust, the front-end system utilizes a permissioned DL with a group of peers. This process requires a consensus of over two-thirds majority for agreement on the contents of the ledger.  
**Voters:** Voters interact with the registration website offered by the TTP. Other interactions with the voters are typically via email as well as a voting website which is offered by the front-end system. In our proposed system, voters are not required to handle their own keys. Instead, the voter-key management component manages this for the voters, and voters use the credentials provided to access the required cryptographic functions. The voter-key management component is run by trusted parties within the front-end system.  
**Election authority:** the EA is in charge of setting up the election, running it, and verifying voter eligibility in collaboration with the front-end system and the TTP.  
Figure 1: Component overview for the PVPBC voting system and interactions between the election actors: the election authority, the trusted third party, the voter and the front-end system. PVPBC is supported by blockchain.  
**Authentication-permissioned DLT:** The authentication DLT manages voter authentication by making use of capability tokens, which are embedded in a smart contract. The authentication DLT is maintained by the TTP.
# Joint Location-Allocation Model for Multi-Level Maintenance Service Network in Agriculture
## 5 Case Study
### Practical Application in Agriculture  
In practical application, agricultural machinery always requires the construction of a multi-level maintenance service network including spare part centres and service stations to supply spare parts and provide maintenance service in a broad geographic agricultural operation area. For example, a famous agricultural machinery manufacturer in China has constructed several spare part centres in Harbin, Tianjin, Fuyang, and so on, and the manufacturer has built more than 300 service stations in China.  
Thus, the proposed MIP-based optimization model and solution approach can be applied to solve the multi-level facilities location and service resource allocation problem, which can reduce service costs effectively and improve maintenance service.  
Furthermore, agricultural maintenance service network software is developed to determine the location of service stations and spare part centres in China. As shown in Figure 9, the practical application in Henan Province is described based on the proposed solution approach.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**NO.** & **The Number of Service Units** & **The Number of Service Stations** & **Time\_Gurobi/s** & **Time\_Benders/s** \\ \hline
1 & 50 & 5 & 21.83 & 25.98 \\ \hline
2 & 75 & 8 & 160.20 & 71.5 \\ \hline
3 & 100 & 10 & 522.41 & 174.41 \\ \hline
4 & 125 & 12 & 2333.45 & 1277.95 \\ \hline
5 & 150 & 15 & 6329.20 & 499.09 \\ \hline
6 & 175 & 17 & 52,400.61 & 781
## 2 Background
### Feature Extraction  
In the feature extraction step, the preprocessed text is transferred into a numerical representation in the form of a vector, also called vectorization. A common approach for vectorization is the bag of words approach, whereby the vector represents the frequency of a word in a predefined dictionary of words. We have used the \"term frequency-inverse document frequency\" (TF-IDF) metric to have feature vectors. TF is the number of times a given term (word or phrase) occurs in the document. TF-IDF stands for term frequency-inverse document frequency and defines a statistical measure used to evaluate how important a word is to a document in a collection or corpus. TF-IDF method formulation is given in the below formula:  
\[\text{TF-IDF(t, d)}=\text{TF(t, d)}\log(\text{N/DF(t)}) \tag{1}\]where 'd' is document and t (term) is the word in a document. TF(t) stands for term frequency, and 'N' is the number of documents in the corpus. Finally, DF(t) represents the number of documents in the corpus containing \"t\". The TF-IDF value increases as the number of times a word appears increases in the document. The TF-IDF method also takes into account the fact that some documents may be larger than others by normalizing the TF term (expressing relative term frequencies instead).  
As the vocabulary size increases, so do the size of the representation. Therefore, in using these text representations, one of the most critical values is the size of the bag-of-words in the model. The bag-of-words size used affects the performance of the classification. To find the optimal bag-of-word size, we performed a grid search.
# Modeling the Transient Response of Thermal Circuits
## 2 Thermal Circuit Model
### Heat Capacity Correction  
Since the \(\
abla\) operator of Equation (1) is applied to the product \(k\
abla T\), and since \(k\) changes within the circuit, a correction must be performed so that the right side of Equation (1) becomes \(k\
abla^{2}T\), which is required for applying the laws of circuits. In fact, for electric circuits, \(\
abla^{2}\phi=\frac{1}{v^{2}}\frac{\partial^{2}\phi}{\partial t^{2}}\)[3; 12], where \(\phi\) is the electric potential and \(v\) is the speed of light. The analogue for thermal circuits is  
\[\
abla^{2}T=\frac{\rho C}{k}\frac{\partial T}{\partial t}. \tag{7}\]  
In this case, we have the first derivative of temperature on the right side. To arrive at Equation (7), one needs to insert a correction in the heat capacity. In fact, taking into account a one-dimensional model, and expanding Equation (1), one has  
\[\frac{\rho C}{3}\frac{\partial T}{\partial t}=\frac{\partial k}{\partial x} \frac{\partial T}{\partial x}+k\frac{\partial^{2}T}{\partial x^{2}}. \tag{8}\]  
Note that the division by 3 on the term on the left side is due to the fact that heat is flowing in only one direction. Equation (8) results in  
\[\rho C^{*}\frac{\partial T}{\partial t}=k\frac{\partial^{2}T}{\partial x^{2}}, \tag{9}\]  
where  
\[C^{*}=\frac{1}{\rho}\bigg{(}\frac{\rho C}{3}-\frac{\partial k}{\partial x} \frac{\partial T}{\partial x}\frac{\partial t}{\partial T}\bigg{)}. \tag{10}\]  
Figure 1: (**a**) Thermal element; (**b**) equivalent thermal circuit for the steady state; (**c**) equivalent thermal circuit for transient response studies.  
By discretizing,  
\[C^{*}=\frac{1}{\rho}\left(\frac{\rho C}{3}-\frac{\Delta k}{\Delta x}\frac{ \Delta T}{\Delta x}\frac{\Delta t}{\Delta T}\right), \tag{11}\]  
where \(\Delta x=(L_{1}+L_{2})/2\), \(\Delta k=k_{2}-k_{1}\), \(L\) is the length of the thermal object and the subscript indices represent the identity of the thermal object. \(\Delta T\) can be approximated to its final steady state value \(\frac{QR}{2}=\frac{QL_{2}}{2k_{2}}\), and \(\frac{\Delta T}{\Delta t}\sim\frac{Q}{\rho C}\). Therefore, one has  
\[C^{*}=C\left(\frac{1}{3}-2\frac{(k_{2}-k_{1})L}{(L_{1}+L_{2})^{2}k_{2}}\right). \tag{12}\]  
Since all thermal objects are in contact with each other, directly or indirectly, we have to include the corrections of all contacts in all thermal objects, so that the general corrected heat capacity of the thermal element \(i\) is  
\[C^{*}=\gamma C, \tag{13}\]  
where  
\[\gamma=\left[\frac{1}{3}-2\sum_{j}\frac{|k_{j}-k_{i}|L_{j}}{(L_{i}+L_{j})^{2} k_{j}}\right]. \tag{14}\]
# Model-Based Virtual Components in Event-Based Controls: Linking the FMI and IEC 61499
## 4 Experimental Evaluation
### Experiment Description  
#### 4.1.1 Component Models  
All experiments were based on a common monolithic reference simulation, which included an On-Load Tap Changer (OLTC) and transformer model. Via the OLTC, the output voltage of the transformer could be adjusted and adapted to fluctuating grid conditions. An OLTC controller model directed the OLTC and initiated tap switching according to the low voltage reading. As soon as the voltage left a predefined interval, a dead time was awaited and the controller initiated one or more consecutive tap switching operations. A simple static three-phased load and controllable voltage source bordered the transformer model and allowed injecting voltage disturbances. The monolithic phasor-based reference model was implemented in MATLAB/Simulink and used components from the Simscape Power Systems Library.  
Based on the reference model, three experimental setups were designed, illustrated in Figure 4. In each experiment, a virtual component, which comprised the OLTC, the transformer, the load, and the voltage source, was instantiated. The FMU defining the virtual component was directly exported from the reference model using the FMI 2.0 for Model Exchange standard via the FMI Kit for Simulink in Version 2.4.0. Two controller implementations with identical functionality were manually derived from the reference model and alternatively used in the experiments.
## 2 Background and Relevant Literature
### Document Clustering Algorithms  
Document clustering techniques discover the natural groupings among a collection of documents based on text similarity. They are unsupervised methods that do not require prior document labeling, where a document is represented by a high dimensional semantic vector space and similar documents tend to stay close and form a dense area. Used as an alternative to text classification techniques, they are suitable for detecting new emerging topics that did not occur before [16]. The K-means and HDBSCAN (Hierarchical Density-Based Spatial Clustering of Application with Noise) algorithms are the two most used clustering algorithms. K-means attempts to partition the documents into a set of k clusters, where k is a pre-defined number decided by the user. This algorithm uses an iterative procedure to assign every document to its closest cluster whilst updating the cluster centroids. However, the number of clusters k is hard to determine unless users have prior knowledge and a deep understanding of their data to evaluate the clustering results and find the correct clusters [17], and the algorithm is not sensitive to noise data, i.e., the documents not belonging to any of the clusters. HDBSCAN [18] produces clusters of data points (in the form of vectors) that have a higher density in the neighboring regions that contain at least a minimal number of data points. HDBSCAN uses GLOSH (Global-Local Outlier Scores from Hierarchies) to detect outliers that are decided by a score (from 0 to 1) when the density of outlier points is much lower than their closest cluster that they are most associated with [19]. Therefore, compared to K-means, HDBSCAN can discover clusters with variable density and different shapes, and achieve better clustering results [20] due to its outlier detection.
# Towards Tamper-Proof Trust Evaluation of Internet of Things Nodes Leveraging IOTA Ledger
## 4 Proposed Methodology
### Node Reliability
#### 4.1.3 Reliability Score  
Once communication consistency and bit error rate are computed, the message-receiving node computes reliability. Reliability computation depends on a weight called \(\rho\). It controls the relative importance of \(CC\) versus \(BER\). This parameter ranges from 0 to 1, where higher values emphasize \(CC\), which is vital in environments where communication frequency is a strong indicator of node reliability. Occasional bit errors can be corrected, but erratic communication patterns signal unreliable and potentially malicious behavior. Therefore, \(\rho\) is set empirically to reflect that \(CC\) is more indicative of long-term trustworthiness. Reliability is computed as follows:  
\[reliability_{i}=\rho\times CC_{i}+(\rho-1)\times BER_{i} \tag{3}\]  
The weight \(\rho\) determines the importance of the metrics \(CC_{i}\) and \(BER_{i}\) and \(0\leq\rho\leq 1\).
## 6 Discussion  
This research focused on addressing the challenge of selecting an effective configuration for heterogeneous CNs designed to solve complex scientific and technical problems. A comprehensive set of analytical models based on mass service theory was developed to evaluate the performance of heterogeneous CSNs. These models serve as a mathematical framework for formulating and solving optimization problems aimed at choosing an effective HCN configuration tailored to specific problem classes. One of the key findings is that the proposed models and algorithms, including the FFGA method for optimization, successfully address the task of selecting the appropriate structure for HCNs. This is validated through testing conducted with LLC \"RITS\" in Krasnoyarsk. As a result of choosing the optimal network configuration, significant improvements were made both in terms of performance and the cost associated with building computational capabilities.  
Figure 7: Non-dominated set of RBC configurations.  
\begin{table}
\begin{tabular}{l c c c} \hline
**Architecture** & \(\overline{T}_{\text{solve}}\) **(s)** & \(\mathbf{A}_{\text{sys}}\) **(%)** & \(\mathbf{C}_{\text{total}}\) **(kUSD)** \\ \hline Homogeneous cluster & 3.6 & 98.5 & 270 \\ \hline Heuristic heterogeneous configuration & 2.9 & 98.9 & 255 \\ \hline FFGA-optimized configuration (this work) & **2.2** & **99.2** & **250** \\ \hline \end{tabular}
\end{table}
Table 3: Numerical comparison of solution time \(\overline{T}_{\text{solve}}\), system availability \(A_{\text{sys}}\), and total hardware cost \(C_{\text{total}}\) across three CN architectures under a \(10^{5}\)-task workload.
## 4 Electronic Sensing Design for MLE-Based Position Estimation  
The hardware electronics of the positioning system consists of a network of BLE modules. One node is attached to the UAV, transmitting its state. The other four sensing nodes are located in the corners of the GLS, receiving the RSSI-BLE signals from the UAV. Below, the UAV's module will be referred to as the rover, while the modules on the GLS will be referred to as sensing modules.  
The electronic platform selected for the UAV target position is the CY8 CKIT-143A and the CY8 CKIT-042-BLE-A interface boards. The rover operates autonomously, and the base station sensing modules are interconnected in a master-slave fashion using the I2C protocol. The master controller module (see Figure 6) receives the information from the A, B, C, and D sensing modules on the corners of the GLS landing platform. This information is transmitted to a PC with the universal asynchronous receiver-transmitter (UART) protocol.
# A Review on Decentralized Finance Ecosystems
## 2 Literature Review
### DeFi Market Mechanism  
DeFi protocols provide services like exchanges, lending, and asset management [3]. Decentralized exchanges (DEXs) facilitate on-chain token trades. The DeFi market employsautomated market makers (AMMs) for asset pricing and control. The DeFi business model revolves around smart contracts, investors, and users engaging in asset movements and generating revenue for the DeFi treasury [35]. Two primary DEX order systems exist: centralized order systems and automated market makers [3]. AMMs employ algorithms to ascertain asset prices, minimizing environmental uncertainty and information asymmetry. They furnish pricing with signaling value, providing generalized asset value information for both buyers and sellers [18]. DeFi operates on a market mechanism, empowering users with control over and the capability to modify various assets using DeFi services [3].  
Figure 1 illustrates the common mechanism of the DeFi business model [36]. The DeFi protocol generates financial services with a percentage of investors and users supporting these services. This process ultimately results in the creation of the DeFi treasury and the locking of assets, which is further elucidated below:  
* **Protocol:** A collection of smart contracts encompassing various facets like PLFs, AMMs, or yield aggregators. These protocols offer open, noncustodial, permissionless, and composable financial services in exchange for nominal fees levied on asset movements, such as borrowing or swapping [36].
* **Investor:** This participant assumes the underlying protocol risk, including potential misbehavior, impermanent loss, or rug-pulls, in return for passive income. Their primary role involves depositing assets and providing liquidity to these financial services [36].
* **User:** Typically, users interact with the protocol in real time, not expecting extended responses. However, in the case of yield aggregators, users may also function as investors. Users initiate asset movements and pay interest rates to the protocol [36].
* **Financial Service:** The linchpin of the entire protocol, this entity locks assets, fulfills asset movement requests, and safeguards against protocol misuse. Additionally, it can act as an investor by leveraging other DeFi protocols, ultimately delivering yields and earnings to other participants [36].  
DeFi services come with costs that impact investors seeking passive income. The additional interest generated through these services encounters a kind of \"tax\" imposed by the DeFi protocol. Conversely, users willingly pay fees to access the platform's services. Consequently, the DeFi protocol earns revenue from both user segments. This market and business model mirrors the concept of \"Two-Sided Markets\", familiar in TradFi [36].