esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### The Need for Online Machine Learning
#### 2.2.1 OML Methods  
There are many ML models that can be adapted to OML implementations. For example, online linear regression is a popular method in OML. Stochastic Gradient Descent (SGD) is used to update the coefficients in the implementation of an online linear regression model, as not all data are available at once. SGD is commonly used to train neural networks.  
Tree-based algorithms are also popular in OML. Trees have nodes for testing attributes, usually by comparison, and branches for storing the test results and making predictions (of a class in classification or a value in regression). One challenge with streaming data is the high storage requirements, as it is impossible to save all data. Trees allow for compact representation, making them popular methods in OML. A BML tree reuses instances to calculate the best splitting attributes (\"splits\"). Therefore, using BML decision tree methods like Classification And Regression Tree (CART) is not effective in a streaming data context.  
Instead, Hoeffding trees are used in OML. They do not reuse instances but wait for new instances to arrive [11]. As an incremental decision tree learner, a Hoeffding tree is better suited to a streaming data context. It is based on the idea that a small sample is often sufficient to select an optimal splitting attribute, supported by the statistical result known as the Hoeffding bound. The Hoeffding tree converges to a tree generated by a BML algorithm with sufficiently large data [9]. However, streaming data can be very noisy, affecting performance (in terms of prediction accuracy) and potentially generating very large trees. Several extensions of the basic Hoeffding tree exist, such as Hoeffding Anytime Trees that work similarly but use a modified splitting procedure.  
The Hoeffding Adaptive Tree (HAT) [12] is an extension of the Hoeffding tree, incorporating a mechanism for identifying concept drift. It employs an instance of the ADWIN [13] concept drift detector at each decision node, to monitor potential shifts in data distribution.
# Horizontal PGA Estimates for Varying Deep Geological Conditions--A Case Study of Banja Luka
## 3 GMPEs for Horizontal PGA and Varying Deep Geology
What should be noted here, regardless of the type of deep geological formation, is that the effects of deep geology are comparable in scale to those of local soil and must not be overlooked. Figure 2 illustrates PGA ranges (median \(\pm\) one standard deviation) calculated with Equation (1) and VIII \({}^{\circ}\)MCS, which was the estimated predominant intensity in Banja Luka during the 1969 earthquake with the epicenter around 10 km from the city center. We can observe that only the combined effects of stiff soil sites and deep geological rock can result in median empirical PGA values comparable to the ones associated with VIII \({}^{\circ}\)MCS. Figure 3 compares the horizontal PGA values recorded in Banja Luka at four separate accelerograph stations [67] with the median and median \(\pm\sigma_{log}\) empirical PGA estimates that were computed using Equation (3). As seen in Figure 3, the recorded horizontal PGA values and the empirical predictions coincide very well. Figure 2: Empirical GMPEs for horizontal PGA, calculated using Equations (3) (solid lines) and (4) (dashed lines) for \(M=6.0\) and different combinations of local soil types and deep geology (see Table 3). The gray area shows the 95% confidence interval (median \(\pm\) 2\(\sigma_{log}\) empirical estimates) for \(s_{L}=2\). The presented scaling equations do not account for potential PGA variations as a function of rupture direction relative to the investigated site. According to a recent study, the directivity phenomenon (the presence of high-velocity pulses) can have a significant impact on masonry structures, such as those in Banja Luka [73]. Additionally, near-field and far-field earthquake effects on low-rise buildings, such as those that are common in Banja Luka, are found to be dependent on local soil characteristics [74]. However, due to a lack of sufficient directivity and near-field data in regional strong motion recordings, this will fall outside the scope of our current research. In this study, we used empirical GMPEs to directly predict site response. Though we did not compare our PGA estimates to the soil response modeled by pattern recognition methods [75] or linear and nonlinear soil response analyses [13, 76, 77, 78, 79, 80, 81], we intend to do so in the future using geotechnical data. Because Banja Luka lacks a network of soil profiles, we may need to interpolate a few available soil profile data [82] to cover the city's territory.
# Evaluating the Effects of Deep Excavation on Nearby Structures Through Numerical Simulation
## 4 Scenarios Simulation Under Adjacent Building Conditions
### Results and Discussion  
Based on the findings of this study, the following implications for practical engineering applications can be drawn, along with insights into how these results address the research questions:  
1. Implications for Practical Engineering Applications
2. Importance of Numerical Simulations: Conducting numerical simulations in deep excavation projects is essential for ensuring construction safety. This approach helps accurately predict potential impacts and reduce risks.
3. Distance Considerations: The distance between the excavation site and neighboring buildings is a key factor affecting diaphragm wall deformation. Engineers should  
Figure 13: Comparison of ground settlement at different loads (MC model): (**a1_a2**) 1 He; (**b1,b2**) 2 He; (**c1_c2**) 3 He.  
carefully evaluate this distance during project planning and execution to prevent damage to adjacent structures.
3. Load Impact: Load factors significantly affect wall deformation and surface settlement. Engineers must rigorously consider load conditions in simulations to accurately reflect real-world scenarios.
4. Awareness of Depth Factors: The depth of neighboring buildings significantly influences the maximum lateral deformation of diaphragm walls. Considering this factor can help avoid overestimating wall deformation and improve project outcomes.
5. Model Selection: The HSS model provided the most reasonable results in the simulations, suggesting that engineers should prioritize its use in deep excavation contexts.
## 5 Natural Scaffolds
### Chitosan  
Polysaccharides are a further group of natural biomaterials and include chitosan, a derivative of chitin which is found in arthropod skeletons. Chitosan shares many of the advantages and disadvantages as collagen-based scaffolds. For example, it is also mechanically weak and has a compressive strength one to two orders lower than cancellous bone [56]. Chitosan has hydrophilic elements and a positive charge which enables it to interact with negatively charged polymers, macromolecules and certain polyanions [26]. This means that it lacks structural stability in aqueous environments--a critical requirement of any bone scaffold [26; 56]. On the other hand, these properties enable chitosan to form a 'hydrogel' that can be exploited for drug-delivery and nanoencapsulation purposes [26; 56]. Some authors even attribute the cationic nature of chitosan with an intrinsic antimicrobial ability [57]. Chitosan is therefore an unsuitable bone substitute when used alone, but can be very useful when blended with other materials to improve its mechanical and structural properties when hydrated. For this reason, chitosan-based composites are being increasingly used in bone tissue engineering [58].
# Privacy Framework for the Development of IoT-Based Systems
## 6 Proposal of Privacy Guidelines
### Proposal
#### 6.3.1 Proposal Part 1: Proposed Privacy Guidelines for IoTS
Our approach empowers developers to provide meaningful insights while minimizing privacy risks. * **Guideline 9: Knowledge Discovery-Based Aggregation. ** This guideline promotes aggregation techniques aligned with privacy-preserving data mining principles. While LINDDUN warns against unnecessary data analysis, it does not propose practical methods for safely extracting knowledge from aggregated data. Our approach bridges this gap by offering developer-oriented strategies that enable useful analytics without compromising individual privacy. * **Guideline 10: Distributed Data Storage. ** This guideline encourages distributed data storage architectures to minimize single points of failure and increase resilience, thereby enhancing privacy protection. While LINDDUN addresses insufficient processing security, it lacks specific guidance on decentralization as a privacy-preserving architectural strategy. Our proposal introduces this concept as a concrete privacy-preserving design pattern tailored to IoT environments, helping developers adopt modern, resilient system architectures that support privacy goals. * **Guideline 15: Privacy by Design and Default. ** This guideline advocates for the proactive integration of privacy principles from the earliest stages of system design, ensuring that privacy protections are embedded by default. Unlike LINDDUN, which refers to general compliance with privacy standards, our approach provides explicit guidance for developers to integrate privacy throughout the entire system development lifecycle, enabling comprehensive and effective enforcement of privacy requirements. These additions reflect original contributions specifically tailored to the constraints and development workflows of IoT systems. Overall, the comparison highlights both the alignment with and the added value of our guidelines relative to LINDDUN, reinforcing their relevance to practical and forward-looking privacy engineering in real-world IoT environments. As illustrated in Table 4, the proposed guidelines demonstrate both alignment with and meaningful extension beyond the existing LINDDUN threat model. To further contextualize this comparison, we highlight four key aspects that distinguish our contributions from those of the LINDDUN framework:  
1. **Transformation into developer-oriented recommendations:** While LINDDUN offers valuable theoretical descriptions of privacy threats, our guidelines build upon these concepts and introduce additional, original elements to deliver concrete, actionable practices for developers. For example, whereas LINDDUN identifies _Insufficient_
## 7 Conclusions  
To develop a more detailed design for energy piles, the thermal resistance of pile should be considered. The heat transfer coefficient at the outer surface of the pile is an important component of the whole pile resistance. It was noticed that different heat transfer coefficient values should be applied as the operational mode switches for the energy pile. As the diameter of energy piles tends to be much greater than that of conventional borehole heat exchangers, the radial displacement due to thermal contraction and expansion of energy piles can be large. In the Lambeth College energy pile with a diameter of 550 mm, the difference of strain could be around 50 \(\upmu\varepsilon\) when temperature variation between heating and cooling is around 20 degrees. This could be 25 \(\upmu\)m movement at the concrete-soil interface. If a 25 \(\upmu\)m small layer of air is located at the surface, which can lead to influence on the heat transfer behavior.  
In the Lambeth College test, the ground temperature was directly monitored. The heat transfer coefficient from the pile to the ground was evaluated using a 3D FE model. Results at five different depths partly supported the hypothesis that the heat transfer coefficient in the heating mode is different from that in the cooling mode due to thermal expansion/contraction of the pile. At 1.5 m and 4 m, the heat transfer coefficient value (\(h\)) was 6 Wm\({}^{-2}\)K\({}^{-1}\) when the pile was heated, whereas it was 3-4 Wm\({}^{-2}\)K\({}^{-1}\) when the pile was cooled.  
In the Shell Centre test, the proposed hypothesis is supported by the GSHP data. Numerical back-analysis was carried out based on the liquid temperature for a TRT trial. When fitting the model results to the monitored liquid temperatures, h = 4 Wm\({}^{-2}\)K\({}^{-1}\) gave a good fit when the pile was cooled, whereas h = 6 Wm\({}^{-2}\)K\({}^{-1}\) gave a good fit when the pile was heated. It can be noticed that the difference in the heat transfer coefficient for the Shell Centre energy pile was smaller than that for the Lambeth College one. The reason could be that the Shell Centre energy pile has a smaller diameter.  
In summary, the back analysis of two energy pile cases illustrates that the heat transfer coefficient at the pile-soil interface can be different between the cooling mode and the heating mode. It is expected that the difference of h is influenced by a number of factors such as soil properties, concrete (grout) properties and the installation method. At present, the numerical studies of GSHPs always assumed that the concrete-soil interface is perfect to pass through the conductive heat transfer. To large size EGSs (e.g., energy piles with big diameter), this interface could lead to considerable impact, hence the determination of h value is of significance.  
Conceptualization, investigation and writing, H.Q.; investigation and writing, Y.Z. ([PERSON]); review and editing, Z.Z., B.W., Y.Z. ([PERSON]), H.C. and X.W. All authors have read and agreed to the published version of the manuscript.  
The APC was funded by Guangdong Austria Cooperative R&D Projects 2018A050501003.
# Considering the Life-Cycle Cost of Distributed Energy-Storage Planning in Distribution Grids
## 3 ESS Installation Site and Capacity Optimization
### The Second-Stage Optimization
#### 3.2.1 Life-Cycle Cost Model
The variable operation and maintenance cost (\(C_{VOM}\)) was changed with the status of real operation status and external variation, including electricity cost, fuel cost, renewable energy subsidies, and CO\({}_{2}\) emission cost. In this study, only electricity cost was considered for simplicity. The annual average electricity cost (\(C_{c}\)), the duration of charging/discharging (\(\Delta t\)), and the number of operation days in a year (\(G\)) have an impact on the annual average electricity cost, which can be represented as:  
\[C_{VOM}=C_{c}\sum_{t=1}^{24}\sum_{i=1}^{N}\left(P_{ESS,i}(t)\cdot\Delta t \right)G \tag{17}\]  
4. Disposal cost  
When the battery reaches the end of their life, the battery modules should be removed from the system and decommissioned, so the disposal costs of the ESS (\(C_{dis}\)) should be taken into account. The cost should be calculated when the battery needs to be replaced. According to Reference [11], there are mature methods for recovering and reusing some batteries like the lead-acid battery. However, the recovery of the Li-ion battery is still in its early stage. The main cost is due to the treatment of the materials constituting the ESS (hazardous and non-hazardous materials), so the disposal cost can be expressed as follows:  
\[C_{dis}=C_{d}P_{ESS,rate}\sum_{\varepsilon=1}^{k}\left(\frac{(1-\beta)^{cn}}{ (1+\sigma)^{cn}}\frac{\sigma(1+\sigma)^{Y}}{(1+\sigma)^{Y}-1}\right) \tag{18}\]  
where \(C_{d}\) is a specific disposal cost per kW of the battery. To sum up, the total cost of ESS in the whole project cycle is consisted of the capital costs, replacement costs, fixed/variable operation and maintenance cost, and disposal cost. It can be summarized as _Cost_ 2:  
\[Cost\ 2=C_{sys}+C_{rep}+C_{FOM}+C_{VOM}+C_{dis} \tag{19}\]The objective function aiming at minimizing the whole life-cycle cost of the ESS is expressed as  
\[f2=\min(Cost\;2) \tag{20}\]  
The fitness of the proposed GA should be the reciprocal of the objective function as in Equation (21). \[Fit\;2=\frac{1}{Cost\;2} \tag{21}\]
## 3 The Proposed Method and Scenarios
### Overview of the System
#### 3.1.1 Entities and Functions  
Our system has three main entities: Trusted Authority Server (TAS), Road Side Unit (RSU), and vehicle. TAS is a Kerberos server that registers all entities, authenticates vehicles, and uploads authentication messages to the blockchain. The TAS has sub-parts, including an Authentication Server (AS) and a Ticket Granting Server (TGS). RSUs are the bridge between vehicles and the TAS. They enable vehicles to enter the network, support data transfer among the network entities, and broadcast information related to traffic conditions. The vehicles play a role as network nodes and data sources. They are equipped with an On-Board Unit (OBU) for transmitting road-related information to RSUs and other surrounding vehicles.
## 4 Simulations and Results Analysis
### Resilience Probability (\(P_{r}\))
#### 4.4.2 Accuracy Analysis of \(P_{r}\)  
As \(P_{R}\) represents the probability that a secure communication link is not compromised after the capture of network nodes, we compute the accuracy by comparing the estimate given by Equation (7) (reminding that for the q-composite scheme, the \(P_{C}(x)\) value used by Equation (7) is computed through Equation (11)) to the actual fraction (obtained from the simulation) of secure links that were not compromised after the capture of network nodes. This fraction is measured in each simulation as follows. Each sensor node is equipped with \(k\) keys out of the key pool \(K\) computed according to the respective values of \(q\) and \(p\). Then, as soon as the secure links between the nodes are established, several nodes are captured one by one, and at each capture, the fraction of non-compromised links is computed.  
We simulate scenarios with different network sizes and different values of \(x\), for \(k=200\), \(q=3\) and \(p=0.33\). The results obtained are shown in Figure 5. In this figure, we can see that the accuracy begins to vary more significantly from approximately 50 captured nodes, a situation in which the impact of node captures on the network links is larger, as shown in Figure 4. This is because the connection probability (\(p=0.33\)) is set to the entire network. This means that it is expected that the network is 33% connected. However, since the connection model is probabilistic, each node may have an individual connection probability different from \(0.33\). Therefore, as the capture of a larger number of nodes has more impact on the existing links between other nodes, the more the fraction of connections of each  
Figure 4: Key management resilience probability (\(P_{R}\)) _vs._ amount \(x\) of captured nodes, for different values of key overlap \(q\), \(k=200\) keys and \(p=0.33\).  
neighbor deviates from the probability \(p=0.33\), the less is the accuracy when \(x\) is large. Nevertheless, the accuracy is still very high (greater than 90%) for all network sizes and values of \(x\).
## 4 Results
### Analysis Result
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline \multirow{2}{*}{**Error Range**} & \multicolumn{2}{c}{**V VitaminB6**} & \multicolumn{2}{c}{**Magnesium**} & \multicolumn{2}{c}{**Dietary Fiber**} & \multicolumn{2}{c}{**Potassium K**} & \multicolumn{2}{c}{**Retinol Equivalent**} & \multicolumn{2}{c}{**V VitaminB12**} & \multicolumn{2}{c}{**Zine Zn**} & \multicolumn{2}{c}{**V VitaminD2D3**} \\  & **tmg** & **tmg** & **(mg)** & **(g)** & **(g)** & **(mg)** & **(mg)** & **(mg)** & **(mg)** & **(mg)** & **(mg)** & **(UD)** \\ \hline \(<\)1\% & 99.8\% & 87.8\% & 93.12\% & 81.8\% & 105.57\% & 92.8\% & 102,151 & 89.7\% & 101,920 & 89.5\% & 109.02\% & 95.8\% & 97.681 & 85.8\% & 110.264 & 96.9\% \\ \(\geq\)1\% and \(\leq\)2\% & 294 & 0.3\% & 2576 & 2.3\% & 1991 & 1.7\% & 452 & 0.4\% & 121 & 0.1\% & 9 & 0.0\% & 151 & 0.1\% & 346 & 0.3\% \\ \(\geq\)2\% and \(<\)3\% & 2042 & 1.8\% & 2091 & 1.8\% & 295 & 0.3\% & 734 & 0.6\% & 91 & 0.1\% & 1 & 0.0\% & 964 & 0.8\% & 3 & 0.0\% \\ \(\geq\)3\% and \(<\)4\% & 295 & 0.3\% & 160 & 0.1\% & 458 & 0.4\% & 162 & 0.1\% & 59 & 0.1\% & 4 & 0.0\% & 1998 & 1.8\% & 3 & 0.0\% \\ \(\geq\)4\% and \(<\)5\% & 27 & 0.0\% & 190 & 0.2\% & 128 & 0.1\% & 56 & 0.0\% & 5 & 0.0\% & 2 & 0.0\% & 424 & 0.4\% & 120 & 0.1\% \\ \(\geq\)5\% and \(<\)6\% & 46 & 0.0\% & 702 & 0.6\% & 308 & 0.3\% & 1118 & 1.0\% & 24 & 0.0\% & 12 & 0.0\% & 95 & 0.1\% & 86 & 0.1\% \\ \(\leq\)10\% & 2877 & 2.5\% & 2045 & 1.8\% & 1154 & 1.0\% & 1391 & 1.2\% & 3627 & 3.2\% & 38 & 0.0\% & 2568 & 2.3\% & 14 & 0.0\% \\ \(\leq\)11-15\% & 1679 & 1.5\% & 1406 & 1.2\% & 296 & 0.3\% & 131 & 0.1\% & 118 & 0.1\% & 1700 & 1.5\% & 1084 & 1.0\% & 1707 & 1.5\% \\ \(\leq\)16-20\% & 576 & 0.5\% & 282 & 0.2\% & 258 & 0.2\% & 396 & 0.3\% & 175 & 0.2\% & 31 & 0.0\% & 2991 & 2.6\% & 10 & 0.0\% \\ \(\geq\)21-30\% & 1016 & 0.9\% & 625 & 0.5\% & 512 & 0.4\% & 475 & 0.4\% & 174 & 0.2\% & 34 & 0.0\% & 1764 & 1.5\% & 29 & 0.0\% \\ \(\geq\)31-40\% & 205 & 0.2\% & 300 & 0.3\% & 63 & 0.1\% & 239 & 0.2\% & 1046 & 0.9\% & 11 & 0.0\% & 324 & 0.3\% & 5 & 0.0\% \\ \(\leq\)41-60\% & 781 & 0.7\% & 597 & 0.5\% & 446 & 0.4\% & 1962 & 1.7\% & 407 & 0.4\% & 171 & 0.2\% & 643 & 0.6\% & 12 & 0.0\% \\ \(\leq\)61-80\% & 574 & 0.5\% & 1594 & 1.4\% & 204 & 0.2\% & 136 & 0.1\% & 141 & 0.1\% & 27 & 0.0\% & 100 & 0.1\% & 11 & 0.0\% \\ \(\leq\)81-100\% & 1896 & 1.7\% & 7775 & 6.8\% & 1878 & 1.6\% & 3980 & 3.5\% & 5097 & 4.5\% & 2215 & 1.9\% & 2532 & 2.2\% & 1368 & 1.2\% \\ \hline \(>\)100\% & 1551 & 1.4\% & 355 & 0.3\% & 257 & 0.2\% & 441 & 0.4\% & 819 & 0.7\% & 545 & 0.5\% & 505 & 0.4\% & 52 & 0.0\% \\ \hline \end{tabular} Note. Each number indicates how much of the data are actually within but margin of error. The percentages indicate the percentage of the total data.  
\end{table} 
Table 6: Discrepancy comparison table of NNFS analysis with the results of this study in the food_wt_and_nutrients (3). While 3 nutrients had a total of more than 95% of the data with an intake error of <2%, 9 nutrients had a total of 90-94% of the data with an intake error of <2%; 12 nutrients had a total of 89.99-80% of the data with an intake error of <2%.
###### Abstract  
Acute and chronic liver failure are clinically significant conditions, and the artificial liver support system (ALSS) is emerging as a novel and effective approach for the clinical management of liver failure. Within this framework, scaffold materials occupy a pivotal position as integral components of the bioreactor. Elevating the performance capabilities of these scaffolds not only augments the therapeutic efficacy of the artificial liver but also lays the groundwork for refining and selecting large-scale hepatocyte culture models. In this study, we introduced a novel hepatocyte scaffold material designated as PET-COL, crafted by coating polyethylene terephthalate (PET) with collagen. This involved a sequence of modifications, including alkaline hydrolysis, EDC/NHS activation and crosslinking, as well as collagen conjugation. The physicochemical attributes of the scaffold were thoroughly characterized by Fourier-transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS), second harmonic generation (SHG), water contact angle measurements, and high-performance liquid chromatography-mass spectrometry (HPLC-MS). Furthermore, an investigation into the material's biological properties was conducted that encompassed SEM (HepaRG growth), fluorescence staining (assessment of cell viability), staining by trypan blue (HepaRG counting), CCK-8 (proliferation of cells), biochemical testing, and immunocompromised assay. Our findings revealed that collagen was covalently bonded to the PET surface, leading to a substantial enhancement in the material's hydrophilicity (\(p<0.001\)). The quantity of collagen coating was determined to be precisely 33.30 ug per scaffold. Human liver progenitor HepaRG cells thrived on the PET-COL material. Compared with the untreated group, cell viability, albumin secretion, urea synthesis, and the expression levels of CYP3A4 and CPS1 increased significantly (\(p<0.001\)), demonstrating remarkable biological vitality. The PET-COL scaffold, as developed in this study, holds immense potential for application in bioartificial livers.  
PET; type I collagen; bioartificial liver; three-dimensional culture; scaffold materials; surface modification; progenitor cells; hepatocytes