esa-sceva/satcom-chunk-collection huggingface dataset:
# Cellulose Nanofibers and Other Biopolymers for Biomedical Applications. A Review
## 4 Outline of Biomedical Applications
### Medical Implants  
Medical implants are designed to replace a damaged organ or structure within the body in order to sustain normal function, e.g., hip replacements. Traditionally materials like metals, ceramics and synthetic polymers have been used in the manufacturing of these devices. Despite being suitably functional as implants, these materials have been shown to be inflammatory and immunogenic [154]. Biopolymers offer a potential solution to resolving these issues as they are biocompatible, biodegradable and non-immunogenic. Bacterial nanocellulose shows promising results for engineering and non-biodegradable ear implants with suitable mechanical properties and patient-specific shapes via 3D bioprinting [155]. Some biopolymers, such as lignin have antimicrobial properties, offering potential as antimicrobial coatings of implants, such as catheters by minimising colonisation of these devices by bacteria, reducing the risk of infection within the body [156]. In terms of stent manufacturing, use of metallic stents can prevent acute occlusion; however, they carry risks of thrombosis and restenosis later on, and patients are required to be on anti-platelet medications to reduce this risk. Bioresolvable stents (BRS) have been designed from biopolymers, such as Poly-L-Lactic acid (PLLA) to overcome these challenges [157]. These stents can treat the coronary lesion and also restore endothelial function, while being reabsorbed months after being implanted, as opposed to metallic stents which remain intact. Powersent(r) is an example of a BRS currently in use; and the stent is designed by co-formulating amorphous calcium phosphate (ACP) nanoparticles with PLLA and 2% Paclitaxel. Early results in animal studies have shown lower rates of restenosis, lower stent recoil, and lower inflammation [158]. It should be noted that the evidence base for BRS is still very limited and mechanical properties can still be improved; therefore, adoption of BRS over more established metallic stents cannot be advocated. Nevertheless, future applications of biopolymers in medical implants are promising. Moreover, PLLA interference screws were tested in patients with a unilateral anterior cruciate ligament rupture, obtaining promising results [159].  
Additionally, temperature-sensitive hemicellulose-based hydrogels were synthesised using hemicellulose obtained from acetic acid pulping of eucalyptus and N-isopropylacrylamide (NIPAAm) through UV photo-crosslinking. This material is promising for use as a smart material for biomedical applications [160].
# A Matrix-Based Proactive Data Relay Algorithm for Large Distributed Sensor Networks
## 5 Model Maintenance Algorithm  
Sensors can make rational decisions by comparing the benefits and energy consumption of broadcast through matrix computation introduced in the previous section. Because of the mobility of sensors, and the dynamic changing nature of data distribution, the three matrices C,D and U need to be updated in time. The more precise the model is, the better the decisions. However, in large sensor networks, considering the massive energy cost on communication, no single sensor can get the precise and global connection and data distribution model but only partial observations.  
In this section, considering the intrinsic energy cost in the operations of these networks, we propose heuristic updating approaches to maintain a local model for each sensor from its incoming messages. With these updating approaches, an integrated decision algorithm is considered to help each sensor make rational decisions with partial observations.
## 2 Materials and Methods  
The original samples were made from a mixture of powders, titanium (Ti) and nickel-boron (NiB) (Table 1).  
\begin{table}
\begin{tabular}{c c c} \hline
**Powder** & **Particle Size (\(\upmu\)m)** & **Purity (Mass \%)** \\ \hline Ti (Polema JSC) & from 0 to 100 & \(\geq\)99 \\ NiB & from 0 to 100 & \(\geq\)99 \\ \hline \end{tabular}
\end{table}
Table 1: Initial powders.  
Titanium (Ti) and nickel-boron (NiB) powders were used as the initial materials for obtaining the samples. The components were mixed in a stoichiometric ratio: 63.5 wt.% NiB + 36.5 wt.% Ti. Then the mixture that was obtained was dried in a vacuum chamber at 200 \({}^{\circ}\)C over a period of 3 h. Samples with a diameter of 23 mm and a mass of 25 g from the obtained mixture were compacted. The compaction pressure was 215 MPa. The obtained compacted samples were placed in an SHS reactor whose volume was 3 litres (Figure 1a). A schematic showing the placement of the samples in the reactor is provided in Figure 1b.  
The sample (1) was installed on the stand of the reactor (2), then a thermocouple (3) was introduced into the sample perpendicular to the lateral surface of the sample, approximately in the middle.  
Then, the reactor was vacuumed and then filled with an inert gas (argon) until the pressure of \(\sim\)2.5 MPa was reached. The combustion reaction was initiated using local heating of the upper surface of the sample via the molybdenum spiral through a stamped-in igniting layer [23; 29]. The composition of the igniting layer was as follows: 80 wt.% Ti + 20 wt.% B. Tungsten-rhenium thermocouples were used to measure the combustion temperature. The video of the combustion process was recorded. Then the combustion products that were obtained were ground into a powder with a ball grinder and steel balls whose diameter ranged between 6 and 15 mm.  
The phase composition of the synthesis products was investigated using a Shimadzu XRD-6000 diffractometer on filtered CuK\(\alpha\) radiation. The phases were detected by comparing the peaks and the obtained diffractograms with the Powder Diffraction File 4 database of the International Centre for Diffraction Data (ICDD(r)). The structure of the synthesis products was investigated on a QUANTA 200 3D scanning electron microscope with focused ion beams fitted with an energy-dispersive spectrometry (EDS) X-ray detector.
# Efficient Multi-Objective Simulation Metamodeling for Researchers
## 3 Experimentation
### Simulation Models
#### 3.1.2 Telecom Simulation  
This model is a publicly available AnyLogic example model [39] of a telecom company operating in a market with competitors. The inputs of interest are call price per minute and aggressiveness (referring to marketing effectiveness). The objectives of interest are as follows: voice revenue (which is specific to people paying to make calls), \"value added\" service revenue (which covers other items besides calls), as well as aggressiveness itself, as the cost of the required marketing strategy to reach a certain level of effectiveness is not represented within the model. In this model, aggressiveness affects the addressable market share, but users decide which company to support based on the prices they observe. This behavior is implemented in the model by referencing tables that presumably represent statistical market research on the probabilities involved.  
The Telecom simulation allows for dynamic adjustment of the inputs, which will result in a transition to a new market equilibrium. For the case study, selected inputs were applied at the start of the simulation. The experimental inputs do not correspond to the initial market state. This requires the simulation to be run until it reaches the new equilibrium, at which time the objectives can be measured.
## 4 Numerical Examples
### Numerical Example 1
#### 4.1.2 Results  
MATLAB MDPToolBox ([PERSON] et al., 2017) was used to solve this problem. Even though the tool primarily solves the MDP model, it has integrated packages (_pomdp_ and _pomdpsolve_) to convert POMDP models into MDP forms using belief states. The test model was formulated for a 5-year operational run of a pipeline. The backward iteration of the [PERSON] equation  
\begin{table}  
\end{table}
Table 4: Cost of failure (modified from the source: [PERSON], [PERSON], and [PERSON] (2016).)  
\begin{table}  
\end{table}
Table 5: Reward function (values).  
method solver was used to solve the problem. Only ten belief states were defined for each deterioration state in order to meet the computation limit of MATLAB. An MSI GT72S 6 QE Dominator laptop with Intel Core i7-6820 and 48 GB RAM was used to run the model.  
For both observation matrix formulations, the solver reported a do-nothing action for state 1, the application of inhibitors for states 2 and 3, pigging for states 3 through 6, and replacement for states 7 and 8 when the POMDP model considered all states that can be fully observed. In other words, this result is what would be attained when the model is formulated as an MDP (i.e., the states are fully observable). The main objective of this modeling was to obtain a recommendation for optimal maintenance operations for different belief states of pipeline deterioration. Accordingly, the result of the model consisted of recommendations for 19,448 belief states combinations. These are obtained from the ten belief states (for each of the eight states) that add up to 1. The optimal action and its cost were given for these belief states. A user interface will be required to read the maintenance action for the specific belief states' combination since the reported combination of belief states is large. Figure 2 gives the general framework of this interface. Do-nothing was recommended at 80.5% (15,662 combinations) and 72% (13,996 combinations) for overlapping and no-overlapping models, respectively. Inhibitors, pigging, and replacement were reported at 1.2% and 1.4%, 3.3% and 3.8%, and 15% and 22.9%, respectively, for the two models.
## 3 Results and Discussion
### Bioaccumulation Factors  
The BAF (Bioaccumulation Factor) represents one of the most used models to predict the partitioning between an exposure medium (such as seawater) and biota [57]. Two possible mechanisms could influence the bioaccumulation of copepods (and hence the BAF values), namely the ingestion of contaminated dietary sources and the diffusive sorption of pollutants from water [58].  
In the present study, BAF values were calculated for the PAH congeners detected both in seawater and in copepods (NaPh, AcPY, Flu, Phe, Ant, FITH and BkFI). To evaluate a possible effect of feeding habits in the bioaccumulation, BAF values were calculated for herbivorous and carnivorous copepods. PCBs were not included since they were seldom detected in all the sea compartments investigated. The logarithms of BAF values are summarized in Table 1.  
The logBAFs obtained revealed positive values for both herbivorous and carnivorous copepods, thus highlighting that, for both taxa, a bioaccumulation from seawater occurred. Average values, 2.8 and 3.4 for herbivorous and carnivorous copepods, respectively, are in good agreement with those discovered by [PERSON] et al. in Pseudodiaptomus marinus copepods [59].  
LogBAF average values of herbivores and carnivores were shown to be statistically different (t-test, N = 7, \(p\) = 0.001), meaning that higher values observed for carnivorous copepods corresponded to a higher bioaccumulation. Even if it is not possible to univocally explain this behavior, this higher value could represent a first biomagnification effect, since carnivorous copepods feed mainly on herbivorous ones. Hence, an increase of pollutant concentrations along the trophic chain is not unexpected.  
The plotting of logBAFs against logKow (Figure 4) exhibited a sound correlation for both taxa (R\({}^{2}\) = 0.92 for herbivorous and R\({}^{2}\) = 0.81 for carnivorous), supporting the hypothesis that the lipophilicity of the molecule provides the main factor driving the type of PAH congeners in copepods.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \
\begin{tabular}{c} **LogBAF\({}_{\text{\bf Herb}}\)** \\ **[L/Kg]** \\ \end{tabular} &
\begin{tabular}{c} **LogBAF\({}_{\text{\bf Carm}}\)** \\ **[L/Kg]** \\ \end{tabular} \\ \hline NaPh & 2.50 & 2.88 \\ AcPY & 2.64 & 2.92 \\ Flu & 2.83 & 3.30 \\ Phe & 2.81 & 3.30 \\ Ant & 2.82 & 3.36 \\ FTh & 2.90 & 3.59 \\ BkFl & 3.19 & 3.79 \\ Taxon average & 2.81 & 3.31 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Logarithmic Bioaccumulation Factors (BAF) from seawater calculated for both herbivorous and carnivorous copepods.  
Figure 4: Linear modelling of LogBAFs against LogKow of PAHs for both herbivorous (black squared symbols) and carnivorous (red circled symbols) copepods.
# Research on Advancing Radio Wave Source Localization Technology Through UAV Path Optimization
## 4 Results
### Circular Trajectory Placement with Varying Radius  
In the previous section, the radius was set to approximately 100 m, which comprehensively encompassed the campus. In this section, the radius was varied to investigate the dependence of localization errors on trajectory radius using RSSI and AOA. Simulations  
Figure 24: Comparison of CDFs for RSSI, AOA, and HYBRID localization models. The HYBRID model outperforms the others in both mean and 90 th percentile error.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Radio Signal Information** & **Mean Error [m]** & **CDF 90\% Value [m]** \\ \hline RSSI & 16.4 & 41.9 \\ \hline AOA & 7.5 & 14.8 \\ \hline HYBRID & 5.3 & 8.7 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mean localization errors and CDF 90% values  
rated, indicating that simply increasing the radius does not necessarily yield better results; instead, an optimal trajectory radius must be determined. One possible reason is that in RSSI-based localization, reducing the radius decreases the Tx-Rx distance, leading to weaker signal attenuation. As a result, noise has a greater impact, degrading localization performance. This observation is consistent with findings by [PERSON] and [PERSON] (2018), who emphasized the need for radius-aware weighting in RSSI-based positioning to address spatial ambiguity.  
On the other hand, Figure 26 shows that AOA-based localization exhibited low sensitivity to trajectory radius. This suggests that increasing the radius does not significantly contribute to reducing angular estimation errors. This may be attributed to the relatively consistent angular spread maintained across the circular trajectory, though very large radii could reduce angular resolution in some NLoS scenarios.  
In addition, our results indicate that the HYBRID method maintains high accuracy across all radii tested, showing robustness against both distance-related and direction-related disturbances. This robustness supports findings by [PERSON] et al. (2019), who highlighted the effectiveness of multi-feature UAV sensing in complex environments. Similarly, [PERSON] et al. (2019) reported that GNSS-based systems benefit from optimized observation geometry, aligning with the concept of optimal radius in UAV path design.
# Applications of Microseismic Monitoring Technique in Coal Mines: A State-of-the-Art Review
## 4 Applications of Microseismic Monitoring in Coal Mine  
### Roof Strata Behavior  
Longwall coal mining causes the deformation, fracture, and movement of roof strata, which influences the surface deformation and the stability of underground excavations. It is generally accepted that the caved zone, the fractured zone, and the continuous deformation zone of the overburden movement in the vertical distribution are distinctly distributed above the excavation panels in the longwall extraction method (Figure 8) [91]. [PERSON] et al. [92] proposed a zoning model that divided the overburden movement into six zones in the vertical direction based on the quantity and energy distributions of microseismic events in the Dongtiahe coal mine. The roof generally fractures and caves periodically with the progress of excavation, which releases the excess accumulated stress. [PERSON] et al. [93] used the fractal patterns of a microseismic event's three-dimensional distribution to divide the roof-fall risk into five threat levels and two hazard levels and further designed an artificial neural network for automatically identifying the roof-fall threat level in real time. The longwall top coal caving mining method is effective and cost-efficient for extremely thick coal seams, and the large mining-height excavation inevitably induces more intensive movement and failure of overburden. [PERSON] et al. [94] studied the influence of a longwall top coal caving operation on the overburden movement above a longwall panel of the Tashan coal mine and divided three zones of the overburden movement in the vertical direction based on the microseismic event density.  
The fracturing and movement of massive and hard strata induced by longwall coal mining is different from that of other overlying strata. The massive hard rock reduces the cavability of roof strata and delays the caving of the large-area hard roof, which releases excess accumulated stress and strain energy violently and may induce catastrophic hazards, e.g., rockbursts and coal and gas outbursts. [PERSON] et al. [95] studied the fracturing and movement of the double-layer hard and thick sandstone main roofs in the Xinhe coal  
Figure 7: Hilbert time-frequency spectrum of a microseismic waveform, revised from [87].  
mine using microseismic monitoring, and the severe strata behavior was characterized by increases in the microseismic event count and energy. [PERSON] et al. [96] integrated the spatio-temporal distribution and the frequency characteristics of microseismicities to study the strata behavior of hard and thick igneous layers in the Yangliu coal mine and evaluated the hard roof fracturing intensity using the ratio of spectral energy of different frequency bands.
# Effective Class-Imbalance Learning Based on SMOTE and Convolutional Neural Networks
## 5 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Models** & **Acc** & **Pre** & **Sen** & **F1** & **G-Mean** & **Spe** & **AUC** & **Kap** \\ \hline TL + NORM. + CNN & 98.92 & 98.93 & 98.92 & 98.92 & 98.90 & 99.07 & 98.87 \\ TL + NORM. + DNN & 98.95 & 98.90 & 98.90 & 98.90 & 98.90 & 98.87 & 98.98 & 98.79 \\ OSS + NORM. + CNN & 98.78 & 98.79 & 98.78 & 98.78 & 98.78 & 98.72 & 98.93 & 98.73 \\ OSS + NORM. + DNN & 98.82 & 98.83 & 98.82 & 98.82 & 98.82 & 98.80 & 98.94 & 98.72 \\ NearMiss + NORM. + CNN & 98.98 & 98.98 & 98.98 & 98.98 & 98.98 & 98.92 & 99.07 & 98.89 \\ NearMiss + NORM. + DNN & 99.01 & 99.02 & 99.01 & 99.01 & 99.01 & 99.05 & 98.99 & 98.89 \\ RUS + NORM. + CNN & 98.33 & 98.34 & 98.33 & 98.33 & 98.33 & 98.23 & 98.75 & 98.26 \\ RUS + NORM. + DNN & 98.22 & 98.23 & 98.22 & 98.22 & 98.15 & 98.59 & 98.14 \\
**SMOTE + NORM. + CNN** & **99.08** & **99.09** & **99.08** & **99.09** & **99.08** & **99.03** & **99.08** & **98.92** \\ SMOTE + NORM. + DNN & 98.96 & 99.00 & 98.97 & 98.98 & 98.98 & 98.99 & 99.02 & 98.78 \\ \hline \hline \end{tabular} Boldface specifies that SMOTE + NORM. + CNN is the most robust model.  
\end{table} 
Table 12: The average performance score of the evaluation metrics of the executed models. tained by the SMOTE + NORM + CNN model. In addition, the results of the proposed SMOTE + NORM. + CNN model are compared with the related works on the Z-Alizadeh Sani dataset, as represented in Table 14.  
Based on Table 14, the outcomes show the dominance of the Proposed SMOTE + NORM. + CNN model compared with other studies. The hybrid model of SMOTE + NORM. + CNN verifies the best performance with 98.57% accuracy, 98.58% recall, 98.57% F1-score, 98.58% precision, 98.42% specificity, and 99.14% AUC. In addition to what I have just explained, I can add that the F1-score metric is reput
## 2 Materials and Methods  
### Chemicals  
Analytical grade BMAA, DAB, TMAO, deuterated trimethylamine \(N\)-oxide (D9, 98%) (TMAO-d9), trimethylamine, kynure acid, dopamine, homocysteine, carbidopa, picolinic acid, L-DOPA, 3-hydroxykynure, corticosterone and formic acid (FA) were purchased from Sigma-Aldrich Italy (Milan, Italy). LC-MS solvent grade acetonitrile was purchased from Carlo Erba Reagents (Milano, Italy). Ultrapure-grade water was produced by a Pure-Lab Option Q apparatus (Elga Lab Water, High Wycombe, UK). Standard stock solutions of the analytes under study were prepared in water/acetonitrile 50:50 at 1000 mg/L and stored at \(-20\,\mathrm{\SIUnitSymbolC}\) until use. A mixed working standard solution was obtained by suitable dilutions in mobile phases. TMAO-d9 was used as an internal standard (IS).
# Privacy-Preserving Byzantine-Resilient Swarm Learning for E-Healthcare
## 6 Security Analysis of PBSL
### Byzantine-Robust  
**Theorem 2**.: _For an arbitrary number of malicious \(\mathbf{MCs}\), in the \(t\)th iteration, the loss between the learned model \(\mathbf{w}_{t}\) and the optimal model \(\hat{\mathbf{w}}\) under no attacks is bounded, i.e.,_  
\[||\mathbf{w}_{t}-\hat{\mathbf{w}}||\leq(1-\rho)^{t}||\mathbf{w}^{0}-\hat{ \mathbf{w}}||+12\beta\Delta_{1}\rho+error \tag{17}\]  
_with probability at least \(1-\delta\), where error is caused by CKKS._  
Proof.: Our PBSL improves the encryption algorithm of FLTrust [55] and extends FLTrust to blockchain, so we directly apply FLTrust's security analysis to PBSL. According to FLTrust, in our PBSL, the loss between the learned model \(\mathbf{w}_{t}\) in the \(t\)th iteration and the optimal model \(\hat{\mathbf{w}}\) is  
\[||\mathbf{w}_{t}-\hat{\mathbf{w}}||\leq(1-\rho)^{t}||\mathbf{w}^{0}-\hat{ \mathbf{w}}||+12\beta\Delta_{1}\rho+error \tag{18}\]  
with probability at least \(1-\delta\), where \(error\) is caused by CKKS. As described in CKKS [34], Equation (19) gives the bound of the encoding and encryption errors.  
\[B=8\sqrt{2}\sigma N+6\sigma\sqrt{N}+16\sigma\sqrt{hN} \tag{19}\]  
Therefore, \(error\) generated by the cipher-text operation of our PBSL can be expressed as follows:  
\[error=B+B_{1}+B_{2}+v_{1}B_{2}+v_{2}B_{1}+B_{1}B_{2}+B_{mult(l)} \tag{20}\]where _error_ from \(\oplus\) is \(B_{1}+B_{2}\), _error_ from \(\otimes\) is \(v_{1}B_{2}+v_{2}B_{1}+B_{1}B_{2}+B_{mult(l)}\), \(B_{mult(l)}=P^{-1}\cdot q_{1}\cdot B_{ks}+B_{scale}\), \(B_{ks}=8\sigma N/\sqrt{3}\), \(P=P(\lambda,q_{L^{\prime}})\), and \(B_{scale}=\sqrt{N/3}\cdot(3+8\sqrt{h})\). Then, when \(|1-\rho|<1\), we have \(lim_{t\rightarrow\infty}||\mathbf{w}_{t}-\mathbf{w}^{*}||\leq 12\beta\Delta_{1} /\rho+error\).  
Therefore, the loss between the learned model \(\mathbf{w}_{t}\) and the optimal model \(\hat{\mathbf{w}}\) is bounded.