esa-sceva/satcom-chunk-collection huggingface dataset:
# Security and Privacy in Physical-Digital Environments: Trends and Opportunities
## 2 The Metaverse and Mixed Reality
### Areas of Application
#### 2.2.1 Culture  
The Metaverse presents a significant opportunity to enhance cultural experiences by merging virtual and physical environments to create highly engaging and authentic interactions. This innovative technology has the potential to overcome traditional tourism's physical barriers and limitations, enabling visitors to engage with historic sites and monuments remotely. Through this, it facilitates new immersive experiences that not only captivate visitors but also transform their traveling and consumption habits.  
MR enables the creation of virtual models of ancient buildings and monuments, enabling visitors to have an enhanced experience when interacting with them. Additionally, this technology may revolutionize the tourism industry by creating digital reconstructions of ancient cultural monuments that would otherwise be impossible to physically reconstruct without damaging the original piece.
## 3 Result and Discussion
### X-ray Diffraction (XRD)  
Inorganic polymers, including geopolymers, are often described as X-ray amorphous [2], since the main characteristic of their XRD spectra is a featureless bump centered at \(\sim\)20 of 20-30\({}^{\circ}\) (Figure 5).  
Figure 4: SEM micrograph of (**a**) raw rice husk; (**b**) metakaolin; (**c**) Palm oil fuel ash (POFA); (**d**) rice husk ash (RHA); (**e**) slag; (**f**) sample 16 before casting; (**g**) sample 16 after casting.  
Rice husk, which contains cellulose, hemicellulose, and lignin, only exhibits the crystalline structure of cellulose. The peaks \(\sim\)16\({}^{\circ}\), 22\({}^{\circ}\), and 35\({}^{\circ}\) are attributed to cellulose [(43; 44)], which exhibits the crystalline structure due to hydrogen bonding interactions and Van der Waals forces between adjacent molecules [(45; 46)]. The RHA shows a broad peak \(\sim\)22\({}^{\circ}\) (20) for amorphous silica and cellulose [(44; 47; 48)]. The major crystalline phase in slag corresponds to CaCO\({}_{3}\)[(49)].  
The POFA peaks are related to SiO\({}_{2}\) (Q--Quartz; C--Cristobalite) [(50)]. The XRD pattern of the metakaolin used in this study shows a product of low crystallinity due to the presence of an amount of quartz (SiO\({}_{2}\)), which is identified by a peak at \(\sim\)25\({}^{\circ}\) (20) [(51; 52)]. The XRD pattern for sample 16 before and after casting belongs to the crystalline SiO\({}_{2}\) phase, which proves that traces of quartz are already present in MK precursor in MK-based geopolymers [(53; 54)]. The reason for the small shift from 26.84\({}^{\circ}\) (20) to 26.62\({}^{\circ}\) (20) could be due to the reaction of SiO\({}_{2}\) during the casting and forming of another silica phase (Folder S1).
# A Simple Direct Method to Obtain Kinetic Parameters for Polymer Thermal Decomposition
## 4 Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Proposed Method**} & \multicolumn{3}{c}{**Lyon Method**} & \multicolumn{3}{c}{**GDM**} \\ \cline{2-9}
**Heating Rate** & \multicolumn{2}{c}{**E**} & \multirow{2}{*}{**A (s\({}^{-1}\))**} & \multirow{2}{*}{**n**} & \multicolumn{2}{c}{**E**} & \multirow{2}{*}{**A (s\({}^{-1}\))**} & \multirow{2}{*}{**n**} & \multicolumn{2}{c}{**E**} \\  & & & & & & & **(kJ mol\({}^{-1}\))** & & **(kJ mol\({}^{-1}\))** & **A (s\({}^{-1}\))** & **n** \\ \hline
2.5 & 192 & \(1.27\times 10^{12}\) & 1.5 & 140 & \(1.03\times 10^{8}\) & 1 & 189 & \(4.80\times 10^{11}\) & 0.84 \\
5 & 175 & \(5.05\times 10^{10}\) & 1.16 & 170 & \(2.25\times 10^{10}\) & 1 & 195 & \(1.45\times 10^{12}\) & 0.87 \\
10 & 181 & \(1.25\times 10^{11}\) & 0.96 & 187 & \(5.14\times 10^{11}\) & 1 & 202 & \(4.07\times 10^{12}\) & 0.77 \\
20 & 160 & \(3.15\times 10^{9}\) & 0.72 & 204 & \(5.59\times 10^{12}\) & 1 & 234 & \(8.14\times 10^{14}\) & 0.82 \\
40 & 184 & \(2.36\times 10^{11}\) & 0.84 & 212 & \(2.57\times 10^{13}\) & 1 & 233 & \(6.90\times 10^{14}\) & 1.04 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of kinetic parameters for the decomposition reaction of PA6.6. Figure 7: A comparison of (**a**) activation energy, (**b**) pre-exponential factor, and (**c**) the reaction order of PA 6.6 decomposition as a function of heating rate by different kinetic methods. Table 4 summarizes the maximum \(dm/dT\) values and temperatures. The calculated results closely match the experimental ones, particularly for the proposed method. Evaluation of the methods for studying polymer degradation was further tested by examining the decomposition patterns of the other five materials at a heating rate of 10 K min\({}^{-1}\). NOMEX undergoes thermal decomposition in several overlapping steps (Figure 6). PVC degrades in two distinct reactions, whereby hydrogen chloride gas is evolved nearly quantitatively by a chain-stripping mechanism (dehydrochlorination) between 500 and 650 K, and the second reaction (700-800 K) corresponds to the pyrolysis of the polyene sequences formed during the previous reaction [15]. PMMA, ABS, and LLDPE decompose via a single thermal reaction. Table 5 shows kinetic parameters obtained with the different methodologies for all polymers. The reaction order was set at 1 for the first step of PVC decomposition in the proposed and GDM methods, as this value is fixed in the Lyon method. This was done because Equation (11) did not yield a realistic value of \(n\), and a minimum error value could not be established within the range of n obtained. The problem, which deserves further study, may be related to the reaction model used to describe the decomposition reactions.
## 3 SAgic-IoT Platform Description
### Layered IoT Architecture
#### 3.2.3 Processing Layer  
This layer processes large amounts of information using policy-based management to trigger alarms and improve production. Additionally, government agencies use processed results (such as SAGARPA in Mexico) to develop production maps to help farmers define the best crops, the best fertilizers, and the quantity of water required for each season or production phase. Each crop type has its own value range for each physical parameter (such as soil temperature and humidity, pH level, environmental temperature, and moisture). These values are recorded on the platform. Table 2 shows an example of ideal physical parameters for tomatoes. Data are evaluated in real time to detect atypical values from the different physical parameters (values outside the defined range) that can represent abnormal conditions. Based on the data, the system can activate other systems (such as irrigation systems).  
The processing layer supports an extensive database that can accommodate massive amounts of data from the sensor nodes relayed through the communication layer. The database is interfaced with data analysis algorithms and real-time scripts for data visualization and evaluation (Figure 3).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Ideal Value** \\ \hline Day temperature & 70 \({}^{\circ}\)F \\ Night temperature & 60–64 \({}^{\circ}\)F \\ Day humidity & 80–90\% \\ Night humidity & 65–75\% \\ pH & Near 7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Physical parameters for tomatoes [89].  
Figure 3: Block diagram of the cloud server in SAGric-IoT.  
The gateway node also implements the processing layer, where the CNN model is stored and processed to identify and classify diseases in the crop.
# Application of Thin-Film Microextraction to Analyze Volatile Metabolites in A549 Cancer Cells
## 2 Results and Discussion
### Study Limitations  
In vitro models are very useful for protocol and methodology development as they offer strictly controlled and reproducible conditions while remaining cost effective and ethical, but they also have considerable limitations. In this regard, only a certain (relatively small) number of adherent cells could be studied at once due to the limited growth surface and capacity of the liquid medium in the Petri dish. The study was also limited by a small number of replicates and application of a sole A549 cell line. However, this work did not aim at the differentiation of cancer cells by means of detected VOCs but was intended to prove the potential application of a novel TFME technique to in vitro model investigation of volatile metabolites. Extensive research with different cell lines and more replicates is planned in the near future utilizing the sampling protocol optimized here.
# A Novel UWB Pulse Expander Using an Integrated Microstrip Splitter, Delay Lines, and a Combiner
## 2 Design and Modeling
### Pulse Combiner  
As mentioned above, the design of the pulse expander incorporates delay lines, which are connected to the pulse splitter's output. The delayed pulses are fed into the pulse combiner at the final stage. In this research, the splitter is constructed as a passive device using microstrip technology and isotropic materials, which means it is a reciprocal device [41; 64] and equalities of transmission coefficients \(S_{21}=S_{12}\), \(S_{31}=S_{13}\), \(S_{41}=S_{14}\) hold true if all ports are matched. It is known that such splitters can be used as combiners: a single signal may be split into multiple outputs, or in the opposite direction, multiple input signals are combined into a single output. In the case of an N-port splitter, the input signal is split into N output ports. When a splitter is used, which has \(R_{\text{iso}}=\ \infty\) in ports 5 and 6 (see Figure 5), it is natural to assume that ports 5 and 6 are not present and are elements of the splitter construction; moreover, these ports do not have to be matched when measuring S-parameters. In this case, using the splitter as a combiner is natural. However, the reciprocity of the device does not imply time-reversal invariance [65] due to losses present in the microstrip construction of the splitter. The use of a splitter as a combiner was tested in simulations and experimentally. In the simulations (see Figure 2, ports 2, 3, 4), pulses with an amplitude of 44 mV and an equal duration of 200 ps, which were time-shifted by 150 ps relative to each other, were fed into the inputs of the combiner. Simulation results can be seen in Figure 8 and experimental results are provided in Section 3. From the simulations and experiments, we can conclude that using the designed splitter as a combiner is possible and provides the desired output pulse.  
Figure 8: Input pulses (port 2 to 4) and output pulse (port 1) of the pulse combiner where the delay between input pulses is 150 ps.  
Figure 7: Transient simulation results—input, reflected, and output pulses of the pulse splitter in the time domain.
## 3 Experiments and Comparison
### Computational Time
In practical application, an infrared scan image frame contains tens of thousands of columns, and the device generates a frame of image in a short time of about 1-2 s. At this point, the processing time for a scanned image utilizing [PERSON]'s [16] algorithm will grow dramatically. By intercepting a portion of the image, the algorithm in this paper calculates the linear correction coefficients. The computational time of the proposed algorithm is mainly in the process of solving the linear model coefficients. The time required to generate an image is rather short. Since the infrared scan technology rapidly scans an image frame and the correction coefficients change very little, it can be considered that the correction coefficients do not change in a short time. Consequently, the production of an image frame often requires a simple linear correction. The time required to correct the infrared scan image will not increase significantly. The algorithm proposed by [PERSON] [18] combines image wavelet decomposition with TV regularization. After wavelet decomposition, it takes 114.6317 s to correct the DC component of an image by TV regularization. This is mainly due to the fact that the DC component of the picture wavelet decomposition has a resolution of 1526 \(\times\) 4096 and the image is huge; thus, the time required to correct it using TV regularization is longer. The algorithm proposed by [PERSON] [17] combines the wavelet transform with the MHE method. It completes eliminating stripes by MHE equalization of the horizontal AC components of the image after wavelet decomposition and guided filtering. In the MHE algorithm, the adaptive selection of parameter s in the optimal Gaussian noise reduction operator \(g_{s}(k)=\frac{1}{s\sqrt{2\pi}}e^{\frac{-\lambda k^{2}}{2\lambda^{2}}}\) takes 184.4410 s, which is a relatively long time. To conclude, the algorithm of [PERSON] [17] and [PERSON] [18] have a high time complexity and are not suitable for nonuniformity correction of infrared focal plane linear array scan images with high resolution and demand for real-time performance.
## 3 System Architecture: Edge-FLGuard
### Local Anomaly Detection Components  
Each edge node executes one or more of the following deep learning models optimized for constrained environments:  
Figure 1: Edge-FLGuard system architecture for anomaly detection in 5G-enabled IoT networks. The framework is composed of three layers: (i) the IoT Device Layer, where telemetry and network data are generated; (ii) the Edge Intelligence Layer, where edge nodes perform local anomaly detection using Autoencoder and LSTM models; and (iii) the Federated Coordination Layer, where a central aggregator orchestrates federated learning rounds by collecting and merging local model updates.  
Figure 2: Data and control flow in Edge-FLGuard. IoT devices send telemetry data to nearby edge nodes, which perform real-time anomaly detection using Autoencoder and LSTM models. Federated learning allows edge nodes to collaboratively update global models without exposing raw data. Secure communication (TLS 1.3), differential privacy, and robust aggregation techniques preserve data confidentiality and defend against adversarial attacks.  
* Autoencoders: Employed for unsupervised anomaly detection by reconstructing expected behavior from input features. Significant reconstruction errors signal potential anomalies.
* Long Short-Term Memory (LSTM) networks: Capture temporal dependencies in telemetry and traffic flows, making them well-suited for detecting delayed or stealthy threats over time.  
At runtime, models infer anomalies in streaming data. During training cycles, each model is updated using recent local observations and contributes only gradient deltas or compressed weight updates to the federation server.
# Biodegradation of Picolinic Acid by _Rhodococcus_ sp. PA18
## 3 Results  
### Isolation and Identification of the PA-Degrading Strains  
Several strains demonstrated a PA utilization from the enriched soil solution after four weeks. Among these strains, one bacterium, designated as PA18, showed a high PA-degrading efficiency. The strain grew in the presence of 6.5% (_w/v_) NaCl, and the D-Xylose, cellobiose, lactose, and D-mannitol could be used as the sole source of the carbon and energy. A phylogenetic tree was constructed based on a BLAST analysis of its 165 rDNA sequence (GenBank accession number: MH681664). The strain PA18 showed a 99%-100.0% sequence similarity to _R. erythropolis_ NBRC 15567\({}^{\text{T}}\) (BCRM01000055), _R. degradans_ CCM 4446\({}^{\text{T}}\) (JQ776649), and _R. qingshengji_ JCM 15477\({}^{\text{T}}\) (LRRJ01000016), and formed a subclade with _R. erythropolis_ NBRC 15567\({}^{\text{T}}\) (Figure 1). Thus, it was identified as a member of _Rbloodococcus_ sp. The genus _Rbloodococcus_ is considered one of the most promising groups of organisms suitable for the biodegradation of compounds that cannot be easily transformed by other organisms [19]. Members of the genus _Rbloodococcus_ exist as abundant indigenous bacterial communities in localities contaminated with various aromatic pollutants [20; 21], indicating that _Rbloodococcus_ spp. may play an important role in bioremediation. Numerous _Rbloodococcus_ have been isolated, for example, _R. pyridinitovans_ PDB9 and _Rbloodococcus_ spp. have been reported to degrade pyridine [22; 23], _Rbloodococcus_ sp. Y22 and _R. rhodocrous_ J1 degrade nicotine or nicotinic acid [24; 25], and _Rbloodococcus_ sp. B1 degrades quinoline or related-compounds [26]. However, no _Rbloodococcus_ species has been shown to degrade PA to date. This study provided new insights regarding PA catabolism and biodegradation by the _Rbloodococcus_ species, which may be used for the bioremediation of polluted environments.
# Union Models for Model Families: Efficient Reasoning over Space and Time
## 7 Results and Discussion
### Results for Trend Analysis (RT2)  
In this experiment, a trend analysis is conducted on an element named X-Goal from a set of individual GRL models and their union model \(M_{U}\). The purpose of this analysis is to study the trend of this goal's _importance value_ attribute and analyze how this value changes over time. Performing this analysis on \(M_{U}\) implies retrieving an element named X of type Goal, annotated with any version number <versions>, which could be a single version, a set of versions, or a range of versions that the element may belong to. With individual models, the search for and retrieval of X-Goal involve each individual model, where the laborious process in practice would involve opening each individual model, searching the desired element, observing its importance value, and closing the current model.  
Figure 26a-c shows the respective T\({}_{\text{ind}}\), T\({}_{\text{MU}}\), and T\({}_{\text{construct}}\) utilised in this experiment, while Figure 26d-f illustrates the time speedups, with and without T\({}_{\text{construct}}\) and the time saved. The results in Figure 26a-c clearly illustrate a linear increase of T\({}_{\text{ind}}\), T\({}_{\text{MU}}\), and T\({}_{\text{construct}}\) as \(SIZE\) or \(INDV\) increases. This is to be expected, as the searching task, which is the core of trend analysis, has a linear time complexity.  
From Figure 26d, it can be noticed that for one \(SIZE\) category, \(SIZE=\) XL, the speedup decreases with the increase of the number of individual models in a family, i.e., \(INDV\). This decrease is mainly due to the consideration of T\({}_{\text{construct}}\) while computing the speedup. Nevertheless, the achieved speedups with T\({}_{\text{construct}}\) are very positive and important. Without considering T\({}_{\text{construct}}\) the speedup becomes more substantial, with a generally increasing pattern as \(INDV\) or \(SIZE\) increases, as depicted in Figure 26e.  
Finally, Figure 26f demonstrates that the use of \(M_{U}\) reduces the time needed to search for elements that belong to a group of models compared to traversing each individual model separately. This is clearly illustrated by the time savings, which substantially increase when \(SIZE\) or \(INDV\) increases.
## 3 Results and Discussion
### Formation of AGEs  
Glycated amino acids are formed in the late phase of the Maillard reaction and are considered their end products [12]. Since the formation also occurs in dicarbonyl compounds, it represents a concurrence reaction to the Strecker degradation. In addition to pyrraline, AGEs MG-H1, CEL and CML were investigated. Here, only the free amino acids were determined. Figure 5 shows the contents of these AGEs depending on the malt modification level (P1 < P2 < P3) in two barley varieties (B1 and B2) during natural and forced aging.  
Pyraline, formed from 3-DG and lysine, was analyzed within 81-188 \(\mathrm{\SIUnitSymbolMicro\text{g}}\)/L. The reaction of MGO with the guanidino group of arginine produces MG-H1, which was quantified at 325-515 \(\mathrm{\SIUnitSymbolMicro\text{g}}\)/L in all samples. In comparison to previous studies, both concentration ranges could be considered low [15; 35]. CEL and CML are derived from the reaction of MGO and GO with lysine, and thus, similar to MG-H1, are only indirectly involved in the degradation of 3-DG. CEL ranged between 111 and 253 \(\mathrm{\SIUnitSymbolMicro\text{g}}\)/L, and CML was found between 69 and163 \(\mathrm{\SIUnitSymbolMicro\text{g}}\)/L. Here, the contents were in the range described for pale lager beer [15].  
In general, no influence of barley variety on the investigated AGEs could be observed. Higher malt modification tended to result in higher content of AGEs. Natural and forced beer aging revealed no consistent trend on the analytes. This confirmed the results of a study by [PERSON] et al., in which the concentration of AGEs also remained constant during beer aging. The authors suspected AGE formation during beer aging as a minor reaction [15]. Furthermore, [PERSON] et al. observed only a minimal increase in AGEs  
Figure 5: Concentrations of free AGEs ((**A**) pyrraline, (**B**) MG-H1, (**C**) CEL, and (**D**) CML) at fresh (FR), forcibly aged (FO), and naturally aged (M3, M6, and M9) beer at different malt modification levels (P1: white bar; P2: grey bar; P3: black bar) in two barley varieties (B1 and B2); \(n=3\).  
derived from proline (N-formylproline and N-carboxymethylproline) during a forced aging experiment [31]. Thus, the formation of the selected AGEs from the precursor compound 3-DG could be considered as subordinate.
# Abnormal Ship Behavior Detection Based on AIS Data
## 4 Results and Discussion
### Experimental Results for Real Datasets
#### 4.2.1 Detection of Abnormal Ship Behavior Based on Spatial Attributes  
The density of the experimental datasets is not uniform in space, and the span is wide in space. A ship always goes to the destination according to a similar driving path, so its movement pattern usually shows the special characteristics of a ship cluster. Based on the method proposed in the third section, we extracted the maritime routes based on the dataset and obtained good results. The experimental results were based on the hyperparameters, including the maximum number of iterations--\(\gamma\) and \(\sigma\)--which affect the efficiency of the code operation, the fitting error, which is the degree of the edge close to the data center, and the degree of fitting, which is the degree of the generated edge burr. Through much exploratory analysis, we set the parameter of the maximum number of iterations to 20, \(\gamma\) to 1, and \(\sigma\) to 0.05. The detection results for the routes are shown in Figure 10.  
Some de facto standardized sea routes are generally followed, taking into account factors such as travel prices and traffic control. Therefore, the distance between the point of the ship's trajectory and the maritime routes--that is, the ship's lateral distance--should satisfy a Gaussian normal distribution. In order to verify this hypothesis, we visually displayed the lateral distance of the experimental data in the form of a histogram, as shown in Figure 11.  
Figure 10: Maritime route extraction results.  
It can be observed from Figure 11 that the ship lateral distance satisfies a normal distribution. Therefore, the probability that the ship belongs to this route can be calculated using the normal distribution probability density. On the data level, the outliers were represented as points with distinct differences from other data points in some attribute features, so we could detect the yaw behavior by calculating the Gaussian distribution of the lateral distance of the ship; that is, if the probability of belonging to the route was lower than a certain threshold, we could consider that it did not follow the channel features to some extent. We selected [PERSON]'s criterion, used in anomaly detection to detect the abnormal points. After the experimental verification, we set the triple standard deviation as the threshold for the judgment standard. We detected six abnormal trajectories with a spatial attribute offset. The detection results are shown in Figure 12.  
The experimental results show that this method can effectively detect the trajectory points with spatial attribute deviation in the normal motion of a ship. However, this detection method is based on spatial attributes and does not take into account the characteristics of a ship's time-related subject attributes.  
Figure 11: Lateral distance distribution of ship trajectory points.