esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Design
### Topic Data Quality Assessment Module  
The quality assessment method can be both supervised and unsupervised and relies on _sensor association_, requiring IoT data from at least three correlated time series. The TQDAM must satisfy the following characteristics at the minimum:  
1. It needs to establish a reference mechanism to measure the correctness of the currently incoming data. This can be performed in two ways: * _Absolute reference_ is where the user creates a set of static reference values, and quality is a measurement of deviation from these reference points. This can take the form of self-assessment with its own historical reference value. However, many sensor data may have randomness, meaning matching with its own history or fixed reference may be difficult. * _Relative reference_ is peer assessment at the current time. If the IoT systems generate a continuous stream of data, then we get a multivariate time series. This mechanism would aim to establish the deviation of one topic's data compared to other topics. In this paper, we focus on peer assessment using unsupervised methods. * A mechanism that only considers some of the latest incoming data points, that is, upgrading the quality score whenever the latest window of data are available. In the case of supervised mechanisms, methods of determining time windows may be different from those used in the paper. * The quality score should be on the same scale. In this paper, we aim to calculate everything as a real number between 0 (worst quality) and 1 (best quality). The scoring should also be uniform across all the topics. * The most important factor is to ensure that each data stream is evaluated in isolation while being compared to others, that is, the quality score calculation for each data stream must be calculated to reflect its own quality only. Normalized Group-Specific Quality Assessment  
In this paper, we consider a covariance and co-relation-based peer assessment method to obtain a Normalized Group Specific Quality Assessment, that is, an assessment of the reliability of a topic with respect to other topics in the same group. Let us consider three correlated topics, namely, \(\tau_{1}=\{y_{1},\,G\}\), \(\tau_{2}=\{y_{2},\,G\}\), and \(\tau_{3}=\{y_{3},\,G\}\), and \(R=\{\tau_{1},\,\tau_{2},\,\tau_{3}\}\) obtained from the correlation matrix (C), which are grouped together, where \(y_{1},y_{2},\) and \(y_{3}\) represent the data associated with each respective topic. If the publishing intervals \(\delta\) of \(y_{1},\,y_{2},\) and \(y_{3}\) are not the same, interpolation in data is required for each pair in the group. This ensures that all data streams in \(R\) are synchronized or have the same number of points, especially if there is more data in one stream compared to the others. The quality assessment begins by applying covariance analysis on \(y_{1},y_{2},\) and \(y_{3}\) for a corresponding window \(w\) (see Equations (4)-(6)).
# MedChain: Efficient Healthcare Data Sharing via Blockchain
## 4 Healthcare Data Sharing
### Key Management  
The application provides each user (a patient, requester, or healthcare provider) a software key case for storing the received cryptographic keys. As shown in Figure 7a, a user first generates and stores his/her key pair (public key and private key) in the key case. The public key is signed and verified by the Certificate Authority (CA). Users also store the keys of others for data sharing. Specifically, when a patient registers at a healthcare service, the patient and the healthcare provider exchange their respective public keys for inventory creation. When a requester needs access to a patient's data, his/her public key will be included in the request and cached in the key case of the patient for a session creation. When a session is revoked, the patient will remove the corresponding key (i.e., \(\mathit{PK}_{req}\)) from  
Figure 6: Process of session-based data sharing.  
the key case. The first time of use, a user will send a copy of his/her public key to the communicating super-peer for message authentication. Moreover, the keys of patients can be shared to family members for emergent healthcare provision. Yet, a more sophisticated solution for emergency access is left to our future work.  
The symmetric keys (i.e., \(S_{data}\) and \(K_{sec}\)) are not stored in user key cases, but stored along with the information they encrypted (an inventory or a session section). The symmetric key storage is illustrated in Figure 7b,c. \(S_{data}\) is stored within the patient inventory and encrypted by \(PK_{sp}\) and \(PK_{pat}\). \(K_{sec}\) is stored within each session section and encrypted by \(PK_{pat}\), \(PK_{sp}\), and \(PK_{rq}\). Since key cases do not need to store these keys, which are temporarily generated in data sharing (esp. for session keys since inventories are likely to be long-term maintained), this design can largely reduce key management overhead. When the data-sharing network connects to a huge number of healthcare providers and requesters, such a benefit will become more important for the reduced space requirement, and more user-friendly especially for the application clients built on mobile devices.
# MaLang: A Decentralized Deep Learning Approach for Detecting Abusive Textual Content
## 5 Discussion  
### Comparison of MaLang with Existing State-of-the-Art Models  
Table 3 depicts the performance comparison of MaLang compared against the best existing state-of-the-art models taken from Table 1, where best performing is based on the accuracy of overall predictions. Logistic regression is added as a reference value since this is a common method for traditional research. Other values of precision, recall, F-score, and AUROC values are provided when available in the previously reported research. Prior research utilized different data sources for analyzing cyberbullying detection systems and as such, only the reported performance measures are used for comparison.  
[PERSON] et al. [65] recently performed an independent comparison of various machine learning and deep learning approaches using various encoding schemas for the detection of online harassment (no age limitations specified). [PERSON] et al.'s findings report an optimal geometric mean, F1 score, and AUROC of: 0.8107, 0.8107, 0.7612; all for systems that utilized GloVe with either a Bi-LSTM or LSTM based deep learning classifier.  
As may be seen in Table 3, the MaLang approach outperforms current existing natural language, parametric statistical, text analytics, and machine learning-based abusive language detection models. While ensemble deep learning methods come close to MaLang's performance, the decentralized approach of MaLang has the additional benefit of preserving data privacy for employees. The decentralized methodology of MaLang also makes this approach scalable to very large organization data networks because the CASE component runs on separate individual devices.
## 2 Methodology  
### Seismic Inversion  
Seismic inversion, recognized as a powerful tool for data interpretation, has been extensively and successfully applied in the development of oil, gas, and coal resources. This technique effectively leverages the resolution advantages of both well log and seismic data. The implementation of acoustic impedance inversion typically relies on density (artificial gamma) and velocity (sonic transit time) well logs, which enable clear identification of coal seams. However, in the studied coal mine, igneous rock formations are nearly indistinguishable by the artificial gamma well logs (Figure 1). The authors calculated a new 'density' log by combining artificial gamma and apparent resistivity curves which would reflect the coal seam and igneous rock simultaneously. The method is known as the dual logging curve fusion technique. It is a curve fusion method that involves the selection, fusion, transformation, and error analysis of logging curves, ultimately resulting in an ideal synthetic density curve for inversion.  
Based on the integrated 3D seismic data and well log data, a model-based inversion approach was implemented to derive the acoustic impedance volume, which serves as a crucial parameter for subsurface characterization. The inversion process, conducted using industry-standard software (e.g., Hampson-Russell 10.4), involved three key steps, each requiring specialized geophysical expertise and rigorous quality control measures. First, seismic horizons were accurately calibrated through detailed well-to-seismic tie analysis, utilizing synthetic seismograms generated from density and sonic logs. This critical step ensured precise time-depth relationships and formed the foundation to establish an initial impedance model. The initial model incorporated both low-frequency trends derived from well data and structural constraints from interpreted seismic horizons. Subsequently,  
Figure 1: Well log responses and new density logs of dual logging curve fusion technique.  
an iterative optimization process was implemented, where the calculated seismic profile was systematically compared with the actual seismic profile through forward modeling. During each iteration, the model parameters were adjusted using advanced algorithms (e.g., constrained sparse spike inversion or stochastic inversion) to minimize the difference between the synthetic and observed seismic data. This refinement process continued until optimal convergence was achieved, typically determined by reaching a predefined correlation coefficient threshold (e.g., >85%) between the modeled and actual seismic traces. The final optimized model, which demonstrated the closest approximation to the real seismic data through both quantitative metrics (e.g., normalized root mean square error <10%) and qualitative assessment (e.g., visual match of seismic events), was then regarded as the definitive inversion result. This high-resolution acoustic impedance volume not only provided crucial insights into lithological variations but also served as a fundamental input for subsequent reservoir characterization and geological modeling workflows.
# Benchmarking Change Detector Algorithms from Different Concept Drift Perspectives
## 4 Material and Methods
### Change Detection Algorithms
#### 4.2.1 Ddm  
The drift detection method (DDM) uses statistical tests to determine whether a drift has occurred, based on the difference between the observed error rate and the expected error rate of a threshold [30]. The DDM algorithm assumes that the data are generated by a stationary process and that a change in the data distribution can be detected by monitoring the error rate over time. This calculates the average error rate and variance of the classifier and updates them as new data points arrive. When the error rate exceeds a certain threshold, which is calculated using the Hoeffding bound, the algorithm signals a drift. The algorithm is adaptive, meaning that it can adjust the threshold as more data points arrive, improving the detection accuracy while minimizing false alarms. DDM is computationally efficient and has been shown to perform well in practice, especially for detecting abrupt drifts.
## 3 Results and Discussion
### Preliminary Energy and Economic Analysis of RS Vaporization through DF  
This section provides a preliminary analysis of the economic and energy aspects related to the proposed valorization processes. As previously noted, the large volume of RS generated by agro-industrial activities presents significant management challenges. In this context, the valorization scenarios proposed in this study aim to improve the cost-benefit ratio of RS utilization. For example, under conditions of 150 ppm magnetite nanoparticle supplementation, the maximum H\({}_{2}\) production achieved was 9.3 m\({}^{3}\) per ton of dry RS. Given the current average price of green H\({}_{2}\) at EUR 7.5/kg [47], the DF process could generate EUR 6.3 per ton of dry RS. Furthermore, considering the cost of DF, estimated at approximately EUR 3/kg H\({}_{2}\)[48], the net value would be EUR 3.3 per ton of dry RS, 1.5 times higher than the value obtained without nanoparticle supplementation.  
However, the use of FeNPs introduces additional economic costs. While FeNPs have demonstrated their potential to enhance fermentative H\({}_{2}\) production, several key factors must be addressed to ensure their economic sustainability. These include optimizing nanoparticle concentrations, avoiding reliance on commercially available powders, selecting appropriate nanoparticle types, sizes, and shapes, and improving distribution efficiency. Additionally, the introduction of organic or non-metallic nanoparticles that are biodegradable and non-toxic is essential to minimize environmental impact and enhance the overall feasibility of nanoparticle use. Lastly, strategies for the recovery, reuse, or potential recirculation of FeNPs must be considered to reduce their economic burden.  
In terms of energy recovery, the higher H\({}_{2}\) production achieved with 150 ppm FeNP supplementation resulted in an energy yield of 118 kJ/kg of dry RS, representing a roughly 50% increase compared to conditions without FeNP addition. Despite these observed benefits, further strategies, such as coupling DF with anaerobic digestion systems, should be explored to improve the overall feasibility and sustainability of the process.  
Moreover, future economic analyses must account for the broader environmental benefits associated with valorizing RS through DF, particularly when coupled with other biotechnologies within a biorefinery framework. This approach not only enhances the economic value of RS but also contributes to mitigating its environmental impacts by offering sustainable management solutions for this abundant agricultural residue.
## 2 Mathematical Formulation
### Model Constraints
#### 2.2.3 Network Constraints  
**Voltage regulation limits:**  
In the case of nodal voltages, Equation (14) ensures that they remain within the limits defined by grid regulations.  
\[V_{i}^{min}\leq v_{i,h}\leq V_{i}^{max}, \tag{14}\]  
\[\{\forall i\in\Omega_{N},\forall h\in\Omega_{H}\}\]  
For this, \(v_{i,h}\) denotes the voltage at node \(i\) during period \(h\), while \(V_{i}^{\min}\) and \(V_{i}^{\max}\) represent the minimum and maximum allowable levels. Voltage deviations are limited to \(\pm 10\%\) (0.1 p.u.) of the nominal system voltage.  
**Current limits for distribution lines:**  
In the case of line currents, constraint (15) sets the limits for current flow through each line to ensure safe operation and prevent infrastructure damage. \(I_{ij,h}\) represents the current flowing through the line connecting nodes \(i\) and \(j\) during period \(h\), while \(I_{ij}^{\max}\) is the maximum allowable current for that line based on its technical specifications.  
\[|I_{ij,h}|\leq I_{ij}^{max}, \tag{15}\]  
\[\{\forall i\in\Omega_{N},\forall h\in\Omega_{H}\}\]  
where \(I_{ij,h}\) represents the current flowing through the line connecting nodes \(i\) and \(j\) during period \(h\), while \(I_{ij}^{\max}\) is the maximum allowable current for that line based on its technical specifications.
## 5 Plasma Precursors
### Argon
Table 13 presents the grading of the parameters relevant to argon plasmas to outline argon's suitability for use in cleaning plasmas. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Summary** & **Parametric Score** \\ \hline Removal of Organic & Argon particles do not react chemically with organic \\ Contaminants Through Chemical & contaminants, especially at the low energy levels used in \\ Reduction and Oxidation & plasma cleaning systems. & \\ \hline Removal of Oxides Through & Argon particles do not react chemically with \\ Chemical Reduction & metal oxides. & \\ \hline Physical Bombardment & Argon plasmas remove organic contaminants and oxides \\  & through physical bombardment. However, they are less \\  & efficient than oxygen (O\({}_{2}\)) or hydrogen (H\({}_{2}\)) plasmas for \\  & organic removal because argon lacks chemical reactivity. \\  & The literature has indicated that argon plasmas are good \\  & for the removal of loosely bound hydrocarbons, light \\  & contamination, such as a fingerprints, and surface \\  & activation. However, argon plasmas are slow for the \\  & removal of thick residues and debris. Carbon residue \\  & debris may also remain on the surface, as argon does not \\  & produce volatile by-products, such as CO\({}_{2}\) or CH\({}_{4}\) \\  & produced by oxygen and hydrogen plasmas, respectively \\  & [110]. The effective bombardment process created by \\  & argon plasma systems also creates an ablative effect on \\  & the device surface, thereby effectively removing thin \\ \hline Plasma Reliability for Sensitive & At the low energies involved in plasma cleaning, no \\ Devices (Minimizing Damage) & damage to either the silicon die or the lead frames \\  & should occur. \\ \hline Plasma Safety: Handling & Ar is a noble gas that is safe to handle. Emissions from \\ Precursors and Emissions Risks & the plasma process should be also safe and would not \\  & need scrubber systems for disposal. \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Grading of parameters for argon plasmas. Summarizing the benefits, argon plasma treatment is particularly effective for bare copper lead frames. Evidence suggests that the induced surface roughening significantly improves adhesion, leading to enhanced reliability, as demonstrated by the absence of delamination after extensive thermal cycling (one thousand cycles reported) [111; 112].
# Fuzzy Neural Networks--A Review with Case Study
## 2 Fuzzy Neural Network Description  
### The Aim and Criteria of Review  
The application of fuzzy logic is convenient wherever it is difficult to create a mathematical model, but it is possible to describe a situation using fuzzy rules, qualitatively. For instance, one can imagine the fuzzy controller rule that imprecisely describes the event, e.g., if it is falling over, push it. For Fuzzy Logic, many industry applications have been found, mainly concerning process control. For medical applications, imprecise language can be translated into fuzzy rules. One of the applications is the use of the architecture of fuzzy neural networks. The topic of fuzzy neural networks is widely covered in publications.  
The aim of this review is to:  
* Analyze information about the purpose of using fuzzy neural networks;
* Examine which types of networks are used for specific purposes;
* Investigate whether fuzzy neural networks can be used similarly to traditional deep neural networks provided by popular frameworks such as PyTorch (version 2.6.0+cu124 ) and TensorFlow (version 2.18.0);
* Examine the implementation code presented to the fuzzy network with a widely used dataset.  
The criteria for selecting the papers were:  
* Year of publication--primarily a six-year span--from 2020 to 2025;
* Use of fuzzy neural networks in practical applications--either well-known problems or the author's own proposed solutions.  
A search process was conducted using the phrase 'fuzzy network,' and then the abstracts were checked to determine if the papers presented any practical implementations.  
These practical applications were particularly important because data science specialists typically use well-established frameworks to build neural networks that solve real-world problems. The year of publication was also a key factor, as data science has seen rapid progress in recent years, making it essential to consider current trends.
# A Multicast Routing Scheme for the Internet: Simulation and Experimentation in Large-Scale Networks
###### Abstract  
With the globalisation of the multimedia entertainment industry and the popularity of streaming and content services, multicast routing is (re-)gaining interest as a bandwidth saving technique. In the 1990's, multicast routing received a great deal of attention from the research community; nevertheless, its main problems still remain mostly unaddressed and do not reach the acceptance level required for its wide deployment. Among other reasons, the scaling limitation and the relative complexity of the standard multicast protocol architecture can be attributed to the conventional approach of overlaying the multicast routing on top of the unicast routing topology. In this paper, we present the Greedy Compact Multicast Routing (GCMR) scheme. GMCR is characterised by its scalable architecture and independence from any addressing and unicast routing schemes; more specifically, the local knowledge of the cost to direct neighbour nodes is enough for the GCMR scheme to properly operate. The branches of the multicast tree are constructed directly by the joining destination nodes which acquire the routing information needed to reach the multicast source by means of an incremental two-stage search process. In this paper we present the details of GCMR and evaluate its performance in terms of multicast tree size (i.e., the stretch), the memory space consumption, the communication cost, and the transmission cost. The comparative performance analysis is performed against one reference algorithm and two well-known protocol standards. Both simulation and emulation results show that GCMR achieves the expected performance objectives and provide the guidelines for further improvements.  
multicast routing; scalable internet; compact routing; adaptability; inter-domain routing +
Footnote †: journal: Applied  
12 August 2021  
Accepted: 13 September 2021  
Published: 17 September 2021