esa-sceva/satcom-chunk-collection huggingface dataset:
# AI Training Data Management for Reliable Autonomous Vehicles Using Hashgraph
## 4 Proposed System
### Decentralized Applications
#### 4.2.1 DApp for Data Uploading  
This DApp establishes a decentralized method for ensuring dataset integrity without storing raw data directly on the blockchain. Instead, it records cryptographic hashes that serve as verifiable proofs of authenticity while significantly reducing storage overhead.  
* Hashing Data: Before data are transmitted to the server, the system applies the SHA-256 algorithm to each dataset. SHA-256 is a widely recognized cryptographic hash function that produces a fixed-length 256 bit output. It is known for its collision resistance, meaning that it is computationally impractical for two distinct inputs to generate the same hash. Additionally, its one-way property ensures that the original data cannot be reconstructed from the hash, thereby enhancing data privacy and security. Even minimal alterations in the dataset yield entirely different hashes, enabling robust integrity verification.
* Storing Hash Values: The resulting hashes are permanently recorded on the blockchain via smart contracts deployed on platforms such as Hedera Hashgraph or Ethereum Geth. Hedera uses a gossip-based protocol in which nodes randomly exchange information, allowing for rapid and efficient consensus with minimal communication overhead. This mechanism provides strong fault tolerance and scalability, rendering it ideal for applications requiring real-time data handling and high throughput. In contrast, Ethereum has traditionally used a Proof-of-Work (PoW) consensus mechanism, where miners solve complex mathematical problems to validate transactions and append blocks to the chain. Although PoW is highly secure, it suffers from high latencies, substantial energy consumption, and limited scalability, particularly under heavy network traffic. Incorporating this DApp into the autonomous vehicle data pipeline guarantees that data authenticity is validated before any AI training process begins. By only storing cryptographic fingerprints instead of raw datasets, the system realizes substantial savings in on-chain storage costs and complexity.
# Methodology for Conceptual Navigational 3D Chart Assessment Based on Eye Tracking Measures
## 2 Materials and Methods
### Data Collection and Processing
#### 2.4.2 Preparation of Questionnaires  
Additionally, as part of an assessment of the level of interest in 3D charting solutions for marine navigation, a questionnaire of survey participants was conducted. The survey consisted of 10 questions, partly general and partly specific, and was divided between 2D map testers and 3D map testers. The survey was mostly constructed of open-ended questions exploring the level of overall satisfaction (2 questions) and the assessment of the interpretive level of the content (5 questions), including object density and styling and personal views (3 questions) on the need for three-dimensional mapping visualisations. The main objective was to explore perceptions about the intuitiveness of the interpretation of the visualisations, the level of distinctness of the cartographical content representation, styling, and views presented on the test, and private opinions about the need for 3D solutions in this regard. The questions were arranged in such a way as to obtain a general view of the demand for 3D solutions in navigation maps and the general level of interpretability of the proposed images. The main difficulty in constructing the questionnaire was the fact that the entire survey concerned a series of scenarios, not a single map. For example, the tester could have had a distorted individual view of the maps when answering the questions. Hence, the construction of questions about specific images was pointless and would not have brought the expected results. The second major limitation of the evaluation method is that the results of the questionnaires may not be objective. On the one hand, the questionnaire gave the authors information on the desirability of this type of visualisation; on the other hand, it may have been biased and generated by the testers' personal feelings rather than a proper environmental need. However, it was only the analysis of the eye-tracking data that showed whether the various proposed symbols reflected the difficulty or simplicity in perceiving and interpreting the visualisation.
## 5 Case Study
### Operation Strategy of EGCS to Improve Dispatchability  
From the above analysis, we can see that the uncertainties of distributed power will have a great impact on the dispatchability margin of the pure power system. Because of the existence of an energy hub or other energy conversion devices, EGCS makes the two networks interact with each other during operation. Therefore, the following analysis will analyze the system dispatchability indexes in two cases and propose an EGCS operation strategy that can improve dispatchability.  
Case 1: Pure power system that the uncertainty of distributed power prediction error is considered. The probability distribution parameters of the prediction error are as shown in Figures 11 and 12 in Appendix A, and the load uncertainty is considered. At the same time, the reliability level of the system is set to 1.5.  
Case 2: The EGCS includes energy hubs, electric drive, and gas drive compressors. Some of the related parameters of EGCS are as shown in Table 11 in Appendix A. It also considers the uncertainty of distributed power prediction error and load uncertainty. The reliability level of the system is 1.5.  
Figure 17 shows the relevant parameters of the system operation cost and dispatchability margin index in two cases.  
Figure 17: Operation cost and dispatchability margin index in two cases.  
As can be seen from Figure 17, the total operating cost of the system in two cases is $1157.91 and $1722.143, respectively. The EGCS contains the operating cost of the natural gas network, so the system running cost is higher than that of case 1. And, the dispatchability margin of the system in case 2 is significantly higher than that in case 1, which indicates that the EGCS has better stability and dispatching margin than single energy network because of the integration of two networks.
# An Adaptive Routing Algorithm Based on Relation Tree in DTN
## 6 Simulation  
In this section, we use ONE simulation software [30] to test the performance of each algorithm. The algorithms we selected for comparison are Epidemic, Spray-and-Wait (L = 8), Prophet (\(P_{init}\) = 0.75, \(\beta\) = 0.25, and \(\gamma\) = 0.98), and Bubble Rap. In order to better evaluate the performance of routing algorithms, this paper mainly uses four indicators: delivery rate, average delay, overhead, and average hop count.
## 2 Materials and Methods
### Fractional Instantaneous Unit Hydrogen  
#### 2.2.1 Fractional Calculus Theory  
Fractional order calculus is a generalization of integer order calculus, which is a theory about calculus of arbitrary order. At this stage, the theory of fractional order calculus is imperfect, and the definition of fractional order derivatives is not unified. Currently, four definitions are commonly used: Grunwald-Letnikov fractional order derivative, Riemann-Liouville fractional order derivative, Caputo fractional order derivative, and Weyl fractional order derivative. Among them, the Caputo definition is often used in engineering applications because the initial conditions to be satisfied in modeling applications and integral transformations are given in the form of integer order calculus, which is easier to obtain in practical engineering problems [95].  
Assuming that the function \(f(t)\) is defined on the interval \([a,t]\) and \(f(t)=0\), \(\forall t>a\), the \(\alpha\)-order Caputo derivative of the function \(f(t)\) is defined as [95]  
\[\prescript{C}{a}D_{t}^{a}f(t)=\frac{1}{\Gamma(n-\alpha)}\int_{a}^{t}(t-\tau)^{n- \alpha-1}f^{(n)}(\tau)d\tau,n-1<\alpha\leq n \tag{1}\]  
where \(\prescript{C}{a}D_{t}^{a}\) is the notation for the derivative of Caputo fractional order, \(D\) denotes the differential, \(C\) denotes the Caputo operator, \(\alpha>0\) denotes the order of the differential, \(a\), \(t\) denotes the upper and lower integration limits, \(n\) is the smallest positive integer not less than \(\alpha\), and \(f^{(n)}(\tau)\) is the \(n\)th order derivative of the function \(f(\tau)\). When \(\alpha\to n\), the Caputo derivative turns into the usual \(n\)th derivative.
# Development of Biodegradable Films Produced from Residues of Nixtamalization of Popcorn
###### Abstract  
Nejayote and pericarp derived from nixtamalization are an environmental problem. Therefore, there is research interest in using these residues as new compostable and environmentally friendly materials. This work aimed to create, characterize (color, thickness, water solubility, water adsorption capacity, microstructure, and degradability), and apply biodegradable films using residues of nejayote and pericarp of nixtamalized popcorn. Three types of films were compared, pericarp (P), nejayote-pericarp (NP), and nejayote (N), and were applied to avocado stored at room temperature. Results showed that the P film was the best because it was significantly (\(p<0.05\)) more transparent (L = 94.75 \(\pm\) 2.21) and thicker (0.27 \(\pm\) 0.02 mm). It had higher water adsorption capacity (162.60 \(\pm\) 14.05%) and higher elastic modulus and toughness (0.004 \(\pm\) 0.001 MPa, 2.25 \(\pm\) 0.25 J/m\({}^{3}\)) values than the N and NP films; in addition, its microstructure showed a roughness value (Ra) of 6.59 \(\pm\) 0.57 nm that was intermediate compared to other films. Moreover, the P coating slowed down the maturing process of avocado and showed a visual effect against fungal infection. All films were generally soft and had a degradation time between 11 and 12 days. The novelty of this study is to provide the alternative of using nejayote and pericarp in a film that is compostable and maintains the lifespan of avocados.  
nixtamalization; nejayote; pericarp; film; avocado; lifespan Article
## 4 Analysis Using Time Domain Method  
To analyse the data sets in the time domain, the simplest time domain method, i.e., SPC control chart was used. Figure 3a shows the control chart for vibration on one of the directions of a normal data type. The horizontal axis in Figure 3a,b represents the time when the data points are collected. The vertical axis of the upper part is the sample mean of the data points, and the lower part is the standard deviation of the data points. There are two red lines in the Figure 3, which are the upper and lower control limits. Normal data points should be located within these two lines. If two consecutive points are out of the control limits, there is a fault-occurring trend. However, we can observe that many red points are out of the control limits, especially in the standard deviation of the data points in Figure 3a. Figure 3b is an abnormal signal pattern; during the data collection,  
Figure 2: Experimental setting—LSM 303 DLHC accelerometer.  
no fault occurred at the beginning of this abnormal data set. However, a lot of red points are out of the control limits in Figure 3b when the data collection process starts. Since out-of-control points are also common in \"normal\" operations, the fault trend is not clearly distinguishable from the acquired data sets. Therefore, it is concluded that the control chart is not an efficacious algorithm for this research.
# A Node Density Control Learning Method for the Internet of Things
## 7 Conclusion  
To solve general problems in the current wireless sensor mobile node density control method, such as long control completion time and large power consumption, a density control learning method for wireless sensor mobile nodes in an IoT environment [23; 24; 25] was proposed. The principle of this method was as follows. Firstly, the relevant hardware IP core in the FPGA development board was configured and the Petalinux system was implemented into it. At the same time, the complex and variable field data processing and control requirements were satisfied by the CC2430 core wireless transceiver module design solution and by using the serial port connection. Then, the advantages of both were combined and the advantages of the WSN were used. Finally, the minimum node density at the time of network connectivity was approximated by exploiting the early and late probability of signal transition between any pair of nodes and by using a normal shadowed regression model and a one-step transition probability matrix. Experimental results showed that our proposed method had a shorter completion time and lower power consumption in comparison to current methods. Lastly, we showed that the node density control accuracy of this method is high, about 90%, which is much higher than that of other methods, which further verifies the superior performance of this method.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Number of**} & \multicolumn{2}{c}{**Calculation Accuracy of Signal Interconnection Probability between Mobile Nodes (\%)**} \\ \multicolumn{1}{c}{**Samples/One**} & \multicolumn{1}{c}{**Method 1**} & \multicolumn{1}{c}{**Method 2**} & \multicolumn{1}{c}{**Method 3**} & \multicolumn{1}{c}{**Method 4**} & \multicolumn{1}{c}{**Method 5**} \\ \hline
100 & 89.8 & 76.3 & 80.2 & 63.5 & 72.5 \\
200 & 90.2 & 74.2 & 79.5 & 65.4 & 74.5 \\
300 & 91.2 & 69.3 & 81.2 & 69.2 & 76.1 \\
400 & 89.3 & 71.2 & 83.5 & 70.2 & 80.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Accuracy of signal interconnection probability between mobile nodes based on different methods.  
**Author Contributions:** Conceptualization, G.S. and S.L. ([PERSON]); methodology, G.S.; validation, S.L. ([PERSON]); formal analysis, S.L. ([PERSON]); writing--original draft preparation, S.L. ([PERSON]); writing--review and editing, G.S.; project administration, and S.L. ([PERSON]).  
**Funding:** The APC was funded by MDPI.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Optimization Results
### Case A: Normal Mode  
In this mode, all of the four main generators were in their ideal condition. Figure 5 shows the output power of PEMFCS with \(\beta=0\). The zero value for \(\beta\) means that there was no importance on the PEMFCS capacity and hence on the amount of hydrogen consumption. In other words, no cost was considered for it. Therefore, the amount of dispatched PEMFCS power was equal to its maximum capacity, i.e., 300 kW.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline Hydrogen price \(c^{h}\) (\$/g) & 0.00341 & battery capacity \(E\) (kWh) & 50 \\ \hline Hydrogen lower heating & & & Maximum charging \\ value \(L^{h}\) (J/g) & 120,000 & power \(P^{C}\) (kW) & 25 \\ \hline Baseline PEMFC mass & & Maximum discharging \\ \(m^{f,hu}\) (kg) & 223 & power \(P^{D}\) (kW) & 25 \\ \hline On-board hydrogen & & Maximum battery state \\ mass \(m^{h}\) (kg) & 73 & of charge (SoC) \(SoC^{\min}\) (\%) & 30 \\ \hline Maximum PEMFCS & 3 & Minimum battery SoC \\ scaling \(d^{f,\max}\) & & \(SoC^{\min}\) (\%) & 90 \\ \hline Minimum PEMFCS & & Charging/discharging \\ scaling \(d^{f,\min}\) & 0.1 & efficiency \\  & & \(\eta^{C}/\eta^{D}\) (\%) & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Proton exchange membrane fuel cell (FEMFC) and energy storage systems (ESS) characteristics [47].  
For the rest values of the weighting parameter \(\beta\), the dispatched power was zero. In other words, as the hydrogen consumption cost increased, the emergency power unit did not inject any power into the main bus during normal condition. Figure 6 shows the changes in PEMFCS size by increasing the weighting parameter \(\beta\). As can be seen, with the increasing importance on the hydrogen consumption cost, the size decreased from 300 kW to 30 kW. Although this change was happening very fast between \(\beta=0\) and \(\beta=0.1\), it remained constant for values greater than 0.1. It can be said that during normal condition, the optimum size of the PEMFCS was very sensitive to its cost rises.  
Figure 7 shows the SoC of batteries during normal mode. As it shows, the ESS did not operate in this mode. All loads were powered from main bus and no loads were interrupted. This result was valid for all values of the weighting parameter \(\beta\).  
Figure 5: The PEMFCS output power for \(\beta=0\).  
Figure 6: PEMFCS power variation with increasing \(\beta\) in the normal mode.
## 2 Digestate as Fertilizer
### Level (1 = very low, 5 = very high)  
Figure 1: Relative nutrient content of digestates derived from different feedstocks. (**a**) Parameter-based comparison. (**b**) Feedstock-based comparison. To optimize fertilizing properties, various post-processing methods may be required [6]. Mechanical separation of liquid and solid fractions enhances digestate usability [38; 44]. Dehydration and drying increase nutrient concentration, while composing stabilizes digestate and reduces phytotoxicity [34; 42; 45; 58]. Pelletization and briquetting modify environmental impact and application behavior [6; 59].  
The study in [60] assessed the fertilization potential of cattle manure digestate after mechanical separation, finding that the liquid fraction had a higher nitrogen fertilizer replacement value due to its ammonium-rich composition, leading to improved immediate crop performance. Conversely, with a higher carbon-to-nitrogen ratio, the solid fraction promoted long-term soil structure enhancement and organic matter build-up [61].  
Composing is a promising post-treatment method to enhance digestate fertilization potential. Despite being cost-effective and widely applicable [62], it has received less attention than mechanical methods [63]. Rather than replacing other post-treatment techniques, composting is often applied after mechanical separation, as it is most effective for solid fractions [64]. During composting, microbial activity converts biogas residues into CO\({}_{2}\), biomass, thermal energy, and a stable, humus-like material [61]. This controlled, self-heating process enhances soil properties, reduces potassium leaching, and mitigates phytotoxicity by converting ammonia into nitrates, improving fertilization potential [65]. Studies indicate that composted digestate application significantly increases crop yields compared to inorganic fertilizers [66; 67; 68]. The results in [63] showed 40% and 100% higher sunflower yields with composted digestate than inorganic nitrogen and untreated digest, respectively, highlighting its potential as a cost-effective alternative to synthetic fertilizers.  
Overall, the use of digestate as a fertilizer offers multiple agronomic and environmental advantages. It supplies essential nutrients such as nitrogen, phosphorus, and potassium, enhances soil structure, and improves water retention. Additionally, digestate can replace synthetic fertilizers, lowering greenhouse gas emissions linked to industrial fertilizer production. However, certain challenges limit its broad adoption. These include the potential presence of heavy metals, pathogens, or persistent organic pollutants--especially when the digestate is insufficiently treated. Nutrient imbalances and ammonia volatilization may also reduce fertilization efficiency and increase environmental risks. Consequently, proper management and, in many cases, additional post-treatment are essential to ensure the digestate's safety, stability, and agronomic performance.
## 3 Analysis of Results
### Compressive Strength  
Figure 7 shows the results obtained. They correspond to the mean values obtained in the tests for each mix. Firstly, an increase in compressive strength can be seen for the different substitution percentages, regardless of the type of waste. The results obtained were above 120 MPa, reaching a compressive strength of 133.8 MPa for 70% GCW-GS. The strength of concrete with GCW-GS was around 5% better than concrete made with GCW-D. There are two factors that can explain this improvement. Firstly, there was a slight improvement in packing produced by GCW-GS, as can be seen in the granulometric curves in Figure 2. Secondly, the presence of Fe\({}_{2}\)O\({}_{3}\) and CaO in the mixture improves pozzolanic activity [28; 29; 30; 31] and reduces autogenous shrinkage [32]. The favorable effect of Fe\({}_{2}\)O\({}_{3}\) occurs for substitution percentages of around 2-3% on the total quantity of cement [30; 35]. This explains the slight decrease in compressive strength for the 100% substitution with GCW-GS (equivalent to 4% of Fe\({}_{2}\)O\({}_{3}\)).  
These increases in the compressive strength are agreement with the results obtained in other studies where the influence of the use of granite waste in different types of concrete has been analyzed [10; 12; 14]. Dr. [PERSON] [12] used granite fines as an alternative to fine aggregates in the manufacture of high performance concrete. He obtained increases in the compressive strength between 6.12% and 22.14% depending on the percentage of substitution. [PERSON] [10] observed an increase in the compressive strength of concrete with a replace up to 30% of the fine aggregate by granite cutting waste. He attributes this improvement to the formation of a dense and compact matrix because the granite particles are fine enough to fill a large part of the voids present in the concrete. [PERSON] et al. [14], also attribute the improvement in their results obtained in compressive strength from UHPC, by substituting micronized quartz for granite residues, to the greater number of fine particles present in these type of waste.  
Figure 6: Density of UHPFRC for both wastes (GCW-D [14] and GCW-GS).
## 2 Hybrid Source Localization Based on TOA and TDOA
### Analysis of Positioning Accuracy Analysis
#### 2.2.2 Cramer Rao Bound  
Cramer Rao Low Bound (CRLB) gives a lower bound on the variance of the unbiased estimator and can serve as an essential comparison indicator for the performance of the estimation algorithm. As can be seen from the previous analysis, the measurement error \(\mathbf{n}\) in Equation (5) can be regarded as the sum of station location positioning error and measurement error, which generally satisfies the zero mean Gaussian distribution, so the probability density function of TOA and TDOA measurements can be expressed as  
\[\mathbf{p}(\widetilde{\mathbf{r}})=\frac{\mathbf{1}}{\left(\mathbf{2\pi}\right)^{\frac{1}{2}}| \mathbf{C}|^{\frac{1}{2}}}\mathbf{exp}(-\frac{\mathbf{1}}{2}(\widetilde{\mathbf{r}}-\mathbf{f}( \mathbf{x}))C^{-1}(\widetilde{\mathbf{r}}-\mathbf{f}(\mathbf{x}))) \tag{27}\]  
CRLB is equal to the trace of the inverse matrix \(\mathbf{I}(\mathbf{x})\) of the Fisher information matrix (FIM) [27]  
\[\begin{split} CRLB=\big{[}\mathbf{I}(\mathbf{x})\big{]}^{-1}=E[\left( \partial\ln(p(\widetilde{\mathbf{r}}))/\partial\mathbf{x}^{T}\right)(\partial\ln(p( \widetilde{\mathbf{r}}))/\partial\mathbf{x}^{T})^{T}]^{-1}\\ =\left(\left[\frac{\partial\mathcal{H}(\mathbf{x})}{\partial\mathbf{x} }\right]\mathbf{C}^{-1}\left[\frac{\partial\mathcal{H}(\mathbf{x})}{\partial\mathbf{x} }\right]^{T}\right)^{-1}\end{split} \tag{28}\]  
where \(\partial\mathcal{H}(\mathbf{x})/\partial\mathbf{x}\) is the transpose matrix of \(\mathbf{G}\) in Equation (19), and then we can obtain the positioning error  
\[\text{positioningerror}=\mathit{Tr}[CRLB] \tag{29}\]