esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Framework Implementation and Validation Strategy
### Validation Approach  
The framework's effectiveness is validated through:  
* Multi-dimensional analysis: the use cases demonstrate the framework's ability to handle spatial, temporal, and hierarchical dimensions simultaneously.
* Scalability testing: the implementation across different metrics and time periods validates the framework's scalability.
* User interaction: the interactive features demonstrate the framework's capacity for dynamic analysis and adaptation.
* Information synthesis: the visualization stories show how complex data can be effectively communicated through structured narratives.  
The subsequent sections present a detailed examination of this implementation strategy through specific use cases in the hospitality sector. These cases demonstrate not only the framework's practical utility, but also its alignment with the theoretical foundationsestablished earlier. By progressing from visualization techniques through advanced machine learning approaches like graph neural networks (GNNs)--that is, machine learning models designed for structured data, enabling advanced relational learning across interconnected policy datasets--and LLMs, we show how each theoretical component manifests in practical applications while maintaining the framework's core emphasis on social order integration and institutional structure preservation.  
In the next sections, we propose several use cases to strengthen the empirical validation of our framework, we incorporate a structured analysis aligned with the framework implementation and validation strategy, as well as advanced tailor-made interactive visualizations with sophisticated preprocessing of data from public datasets and LLM policy generation with LLMs. Specifically, for instance, we expand the hospitality sector case study by performing an ADR market segmentation analysis using graph neural networks (GNNs). This implementation aligns with our predictive modeling and intervention design components. Our results demonstrate that seasonal pricing fluctuations, customer segmentation, and regional influences are key determinants of market stability. These inclusions provide a data-backed validation of our framework, reinforcing its applicability beyond theoretical discussions. Additionally, we explore additional use cases from related policy domains where institutional intelligence emerges from advanced visualization techniques, with sophisticated prior preprocessing of data to extract a meaningful narrative and interactive plots coded with plotly in Python and presented as a public web app in Streamlit.
## 4 Proposed Method
### Procedure of the Proposed Method
#### 4.4.2 How REP Calculates an NEA  
To calculate an NEA, a REP obtains information on the location and the number of its one-hop neighbors. Each mobile host broadcasts its location information every configuration parameter time \(T_{b}\) to its one-hop neighbors via D2D communication. The mobile host uses the location information broadcast by its one-hop neighbors to track their number and location.  
Next, the REP calculates an NEA based on the location and number of its one-hop neighbors. The REP calculates the center O and the radius \(r\) of a circle that covers the locations of itself and its one-hop neighbors and \(N\), the number of mobile hosts including itself, and records (O, \(r\), \(N\)). The center O is set at the centroid of the locations of the REP and its one-hop neighbors, and \(r\) is set at the maximum distance between the center O and the REP or its one-hop neighbors. The REP sends the recorded (O, \(r\), \(N\)) values as an NEA in a Bid Invitations Query message.
## 2 Materials and Methods
### Picture Learning Condition  
We chose to use emotional pictures from the Open Affective Standardized Image Set (OASIS), which is an open-access picture bank containing 900 pictures with normative measurements of two facets: arousal and valence [54]. While the participants' brain responses were measured with the EEG, they were shown 20 negative, 20 positive, and 20 neutral pictures in a random order. Pictures of each of the three emotional categories were balanced by low, medium, high arousal ratings. Each picture was shown on a black screen for at least 2000 ms and as long as required until the participant indicated by a button press whether the picture would rather belong to spring, summer, fall, or winter. We required a response to maintain attention and ensure processing of the pictures, and to facilitate memory encoding. The inter-trial interval was 1000 ms plus a variance of 0-10 screen flip intervals. During the inter-trial interval, a white fixation cross was presented at the center of the black screen.  
Afterwards, participants were asked to freely recall the pictures. Their responses were recorded manually by an experimenter and matched with the list of pictures. Counts for remembered pictures within each valence category were then used for analysis of emotional memory effects. Number of remembered images per valence category can be found in the supplementary data file.
# LoRa, Zigbee and 5G Propagation and Transmission Performance in an Indoor Environment at 868 MHz
## 3 Methodology
### Zigbee Measurement Descriptions  
To study the quality of the signal presented by communication using Zigbee technology, two XBee 868 LP modules were used that work at a frequency of 868 MHz. Both modules were configured to exchange information through point-to-point communication in transparent mode. This type of communication gives the same result as if the two modules were connected by a cable, but wireless communication makes that physical cable unnecessary. XBee modules can be connected to a microcontroller via serial communication, thus allowing the information received by the module to be processed and, therefore, to monitor or even control remote devices by sending messages through their local XBee module. In this case, a Pycom Lopy4 microcontroller was used for each Xbee module. In this way, one of the microcontrollers sends data via serial communication to its local XBee module so that it can later be wirelessly transmitted to the other XBee device and processed by the other microcontroller. This device configuration can be seen in the block diagram shown in Figure 5.  
In a similar way to communication by LoRa technology, a program was generated for Zigbee that would allow the data sent and received to be processed.
## 5 Performance Analysis and Simulation
### Security Performance Analysis  
For an attacker, he needs to trace to the PN so as to find the real source node. More PNs can bring more diversities and uncertainties in routing which will increase the difficulty for the attacker to track back and provide longer safety period for the true source node. In this paper, the security performance indicator is also represented by the safety period. The safety period in our latter simulations is represented by the number of packets sent by the source node before the panda is captured.
## 4 Discussion
### More Exploration of the Acceptability and Application of Sensing Technology Needed  
Considering that only five studies were identified and selected for the review, remote surveillance appeared to receive less attention than wearable technologies. Most studies explored a range of technologies, which suggested limited evidence to explore the acceptability of specific technology types. Video monitoring was more commonly discussed than other technologies in the reviewed articles. Therefore, the lack of comments concerning other technologies represented a potential research gap in understanding receptibility.  
Limited research on other technology types could indirectly and negatively impact remote sensing technologies' overall acceptability. Compared with other relatively non-intrusive sensing technologies, video monitoring is associated with more privacy concerns [59; 60]. However, the lack of research on other technologies that could perform some functions of video monitoring means users are less informed about their possibilities or presence. The absence of such knowledge might facilitate the notion that video monitoring, a technology associated with stigma, was the only technology for remote sensing. These negative connotations might prevent adoption.  
Figure 4: A summary of common themes for RQ2.  
Among the reviewed studies, none explored the acceptability of vibration and/or acoustic technologies in assisted living. Therefore, the exploration of their acceptability could be a future research initiative.
## 2 System Model  
A typical wireless link from the sender to the receiver is described in Figure 1, which includes all necessary modules to be used for the analysis in this work. The transmission starts from the information source, who generates information packets. As suggested in [15], the arrival of URLLC information packets is usually assumed to follow periodical model with fixed length. Here, let \(k\) denote the length of information packet. Then, an information packet will be transmitted out through one link or be duplicated and transmitted through multiple links. Data duplication, instead of Automatic Repeat Request (ARQ), is preferred for URLLC traffic to meet the reliability requirement, because ARQ requires retransmission after feedback from packet error detection at the receiver causing higher delay. Therefore, data duplication has been proposed for use in 5G NR system and beyond to achieve low latency while ensuring high packet success probability.  
In each transmission link, the information packet will first goes through a queueing module, where it may go out directly when the system is idle or it may wait for some time if the system is busy. In this module, packet length will not be changed. Next, in the coding module, redundancy bits are added into the information packet in order to compensate for possible errors. Let \(k\) denote the length of a URLLC information packet in unit of bit and \(r_{c}\) denote the coding rate, the length of the coded symbols (denoted by \(n\)) will be \[n=\frac{k}{r_{c}}. \tag{1}\]  
After modulation, time-frequency resources are allocated for transmission through the wireless channel. The amount of allocated resources and channel quality will greatly impact the success probability and delay. At the receiver side, reverse operations are conducted to recover the information packet. When duplication transmission is applied, the information packet will be received correctly as far as one of the duplications is received correctly. In the next section, the delay and reliability of these URLLC packets going through the system described in Figure 1 will be analyzed.
## 3 Results and Discussion
### Type I PKS Phylogeny
[14] that fungal TI-PKS genes could have been acquired by an ancient horizontal gene transfer event between bacteria and fungi. Additionally, our results suggest that ancestral TI-PKS genes may have been partially reducing. Under this scenario, NR- and R-PKS evolution could have been connected to domain structure modification (such as gaining SAT in NR-PKS) and subsequent functional diversification. However, this cannot be concluded with certainty without a greatly expanded sample of genomes from other fungal groups, including genomes from early branching fungal lineages and comprehensive ancestral state reconstructions.
## 2 Sample Extraction  
Direct injection of beer and wine into GC is not suitable because the sample contains large amounts of water and non-volatile components that are harmful to the GC column [59]. Besides, some VOCs need to be concentrated to reach the sensitivity of the detector. However, since the aromans of beverages range in concentration from mg/L to ng/L and from polar to non-polar, no single sample preparation method is universally suitable to screen all volatiles in an untargeted analysis. Hence different sample preparation techniques are involved before the GC injection [60]. The physicochemical properties of the common VOCs in the beverages are listed in Table S2. The choice of the VOC extraction method is based on the physicochemical properties of the interested volatiles. Different methods differ in extraction mechanism and absorption/collection material, meaning they require different optimizations. And, most importantly, applying different extraction methods results in different analytical sensitivity to one volatile compound and different coverage of the entire VOC profile [36; 51; 61]. In this review, we will discuss five common extraction methods and their possible further applications to beer and wine. Before introducing the sampling techniques, it is necessary to classify the three types of extraction procedures: (i) single extraction, (ii) repeated stepwise extraction, or (iii) continuous extraction. In the case of a single extraction step, the sample is placed in a closed, multi-phase system. After a long enough time, the system reaches equilibrium, when the exchange of analytes among the phases no longer affects their concentration. The concentration value is determined by the equilibrium constant (e.g., Henry's law constant) among multiple phases [62]. Most modern sampling techniques can be applied with this approach, for example, headspace (HS) and HS-solid phase microextraction (SPME), in-solution SPME, liquid-liquid extraction (LLE), and stir bar sorptive extraction (SBSE). If exhaustive sampling is the purpose, stepwise extraction or multiple extractions can be
# Modeling Volatility Characteristics of Epileptic EEGs using GARCH Models
## 6 Results
### Half-life and Confidence Intervals  
To examine the volatility across all channels for the different segments of each dataset, the volatility half-life was calculated for each model along with 95% confidence intervals using the delta and asymptotic methods outlined above (Equations (6) and (7)). Only models with both significant ARCH and GARCH coefficients were used, and confidence intervals with negative confidence limits were used. There were noticeable differences between the seizure and non-seizure channels in the pre-seizure and awake datasets (Table 2a,b and Table 2a,b). The half-life estimates for RAST3 and RAST4 were between one and two for all but three segments of the both datasets, indicating a volatility persisting half as long as most other channels, and at least four times shorter than at least half of all other channels. In examining the CIs for these two channels, they overlap with at most five of the CIs of the non-seizure channels using the delta method (Table 2a,b) and at most one using the asymptotic method (except for the segments where the half-life was greater than 2). For RMST4, the half-life for the segments varied, ranging from 6.33 to 268.82, with more than half below 6.50; the CIs for these overlapped more than half of the CIs for the non-seizure channels for all of the awake segments and approximately half for \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Seizure Channels**} & \multicolumn{2}{c}{**Non-seizure Channels**} \\ \hline  & **RAST3** & **RAST4** & **RMST4** & **Half Life** \\ \hline \multirow{4}{*}{**Segment**} & **HL** & **HL** & **HL** & **Median** \\  & **95\% CI** & **95\% CI** & **95\% CI** & **Min** \\  & **Overlapping** & **Overlapping** & **Overlapping** & **Max** \\  & **Interval Count** & **Interval Count** & **Interval Count** & **Interval Count** & **Max** \\ \hline \multirow{4}{*}{8} & 20.30 & 380.8 & 7.92 & 24.55 \\  & (14.19,26.40) & (134.7,626.9) & (1.45,14.39) & 4.94 \\ \cline{1-1}  & 36 & 3 & 41 & 98.15 \\ \cline{1-1}  & 10.76 & 5.94 & 6.42 & 25.01 \\ \cline{1-1}  & (7.27,14.24) & (4.40,7.49) & (2.5,10.34) & 6.29 \\ \cline{1-1}  & 37 & 23 & 32 & 106.96 \\ \cline{1-1}  & 25.37 & 44.89 & 6.39 & 14.58 \\ \cline{1-1}
10 & (12.71,38.03) & (20.83,68.94) & (1.64,11.14) & 3.16 \\ \cline{1-1}  & 32 & 29 & 40 & 164.74 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._the pre-seizure data (for both methods). The CIs for RMST4 only overlapped the CIs of RAST3 and RAST4 in segment 6 of the pre-seizure data using the delta method.
# Analysis of Sequential Pretreatments to Enhance the Early-Stage Biorefinery Designs
## 3 Results
### Heuristic Analysis  
After evaluating each indicator based on the literature and simulation data, the scoring and weighting for the heuristic analysis were performed, as shown in Table 6. The heuristic analysis concluded that DA with WAO and DA with alkali sequences are the most theoretically optimal for cellulose isolation, with total scores of 7.8 and 7.5, respectively. Therefore, sequencing should first involve a hemicellulose fractionation step through acid-catalyzed pretreatments followed by lignin removal pretreatments. Indeed, this trend was also observed for the sequence of SE and WAO. In contrast, performing alkaline pretreatments first for lignin solubilization leads to a small fractionation of pentoses, significantly decreasing operational indicators such as \(\mathrm{I_{Y}}\), \(\mathrm{I_{V}}\), and \(\mathrm{I_{A}}\). These results agree with the literature, showing a decrease in cellulose recovery when alkali is used as the first step in a pretreatment sequence [13]. Similar results were also observed when using weaker acids, such as peracetic acid, best used for delignification processes [34]. Based on the results in Table 6, it was concluded that \(\mathrm{I_{E}}\) contributes largely to the discussion of the heuristic analysis (\(\omega_{i}\) of 17.8%), gradually decreasing the total score when performing kraft processes. \begin{table}
\begin{tabular}{l l c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Fraction**} & \multirow{2}{*}{**Sequential Scheme**} & \multicolumn{6}{c}{**Indictors**} \\ \cline{3-13}  & & \(\mathbf{I_{R}}\) & \(\mathbf{I_{A}}\) & \(\mathbf{I_{CR}}\) & \(\mathbf{I_{S}}\) & \(\mathbf{I_{V}}\) & \(\mathbf{I_{V}}\) & \(\mathbf{I_{WD}}\) & \(\mathbf{I_{E}}\) & \(\mathbf{I_{CapEx}}\) & \(\mathbf{I_{OpEx}}\) & \multirow{2}{*}{**Total**} \\ \cline{3-13}  & & & & \multicolumn{6}{c}{**Weight Factor (w\({}_{i}\)/\%)**} & & & & & \\ \cline{3-13}  & & **8.5** & **9.5** & **11.6** & **7.6** & **12.5** & **4.7** & **2.5** & **17.8** & **12.7** & **12.7** & \\ \hline \multirow{6}{*}{Cellulose} & DA + alkali & 8.4 & 8.7 & 7.3 & 5.3 & 7.0 & 5.5 & 8.6 & 7.7 & 7.7 & 8.1 & 7.5 \\  & DA + WAO & 8.4 & 9.9 & 9.7 & 4.2 & 7.2 & 5.3 & 7.7 & 7.7 & 7.7 & 8.1 & 7.8 \\ \cline{1-1}  & Kraft + DA & 8.1 & 9.3 & 8.6 & 5.6 & 6.3 & 5.1 & 7.7 & 3.8 & 7.7 & 8.6 & 7.0 \\ \cline{1-1}  & Kraft + SE & 8.8 & 9.1 & 8.2 & 7.0 & 7.2 & 5.0 & 7.7 & 3.8 & 7.7 & 8.6 & 7.2 \\ \cline{1-1}  & Kraft + WAO & 8.4 & 9.6 & 9.1 & 5.6 & 5.2 & 5.2 & 7.7 & 3.8 & 7.7 & 8.6 & 6.9 \\ \cline{1-1}  & SE + WAO & 9.1 & 9.8 & 9.5 & 4.4 & 7.2 & 4.6 & 7.7 & 5.8 & 7.7 & 6.7 & 7.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Total heuristic analysis of the proposed sequential pretreatments for isolating or removing lignocellulosic fractions.
## 6 Test and Validation  
ANOVA is a test used to decide whether there is a semantic difference between more than two independent groups. Hence, the ANOVA test, also denoted as the analysis of variance, was performed to examine the statistical significance of the data acquired to verify the results produced by the independent values. The n-way ANOVA test was employed to determine the statistical validity of applying deep learning algorithms in place of machine learning algorithms in the statistical test since the variable value was more than one [52].  
In the study carried out for data sets with large record numbers as illustrated in Table 13, a \(p\)-Value of 0.008, which is a much smaller value than 0.05, indicates that running tests with DL algorithms with 95% confidence displayed a statistically higher performance than ML algorithms.
## 3 Derivation of a New Finite Element
### Calculation of Shear Force Functions  
Even more options are available when deriving shear force functions. In the formal mechanical approach, the first derivation of bending moment functions (related to the third derivative of transverse displacements) is combined with the first derivative of transverse displacements (\(V_{y}(x)=M_{z}{}^{\prime}(x)-k_{G}\cdot v^{\prime}(x)\)). However, the availability of two approaches for bending moment functions evaluation consequently leads to two shear force distribution functions. The first approach is a formal approach but also the less accurate one, where the bending moment functions are obtained from the second derivative of transverse displacement functions. Namely, although the resulting shear force function is still a quadratic polynomial (due to the derivative of transverse displacement function), it should also be noted that the derivation of bending moments only contributes a constant value. In the second approach, the derivatives of bending moment functions obtained by the interpolation are coupled to the first derivative of transverse displacement functions. In this way, the resulting shear force function is still quadratic polynomial but slightly better than in the first approach. Furthermore, both approaches incorporate the derivatives of transverse displacement functions and obtain two shear force functions within the element due to the presence of the crack.  
However, since the quality of the polynomial functions, and, consequently their results degrade with each derivation, the shear force functions are rather obtained by direct interpolation in the last approach, where either H1 (cubic) or H2 (quintic) Hermite polynomials are implemented. This purely mathematical approach uses nodal shear forces and their derivatives in interpolation. If H1 (cubic) Hermite polynomials are implemented, just the first derivatives of nodal shear forces are required, which are evaluated as:  
\[V_{y}{{}^{\prime}}(0)=q_{L}-k\cdot Y_{1} \tag{36}\]  
\[V_{y}{{}^{\prime}}(L)=q_{R}-k\cdot Y_{2}. \tag{37}\]  
Nevertheless, the implementation of H2 (quintic) Hermite polynomials is also possible, where the required nodal second derivatives of shear forces are evaluated as:  
\[V_{y}{{}^{\prime\prime}}(0)=q^{\prime}(0)-k\cdot\Phi_{1} \tag{38}\]  
\[V_{y}{{}^{\prime\prime}}(L)=q^{\prime}(L)-k\cdot\Phi_{2}. \tag{39}\]  
Both interpolation approaches that preserve nodal shear forces values (\(V_{y1}\) and \(V_{y2}\)) produce a single shear force function over the whole element.
## 2 Materials and Methods
### Data Used  
In this study, multi-sensor satellite/gridded products were used to achieve the different objectives of the present study (Table 1). The satellite/gridded datasets (e.g., Landsat, gross primary productivity, net primary productivity, and vegetation transpiration) were pooled and processed in Google Earth Engine (GEE) cloud platform. The specification and details of the individual datasets are mentioned in further sub-sections.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & **Satellite/Model** & \multirow{2}{*}{**Data Acquisition (Year)**} & \multirow{2}{*}{**Purpose**} \\  & **(Resolutions)** & & \\ \hline \multirow{2}{*}{Surface reflectance} & Landsat 5-TM, Landsat 8-OLI & \multirow{2}{*}{Median image of 1990, 2000, 2010, 2020} & LULC change \\  & (30 m, 16 days) & & \\ \hline \multirow{3}{*}{NPP} & MODIS Terra & \multirow{3}{*}{Yearly data from 2001 to 2020} & \multirow{3}{*}{NPP dynamics} \\  & (500 m, Yearly) & & \\ \hline \multirow{3}{*}{GPP} & PML\_V2 & \multirow{3}{*}{Yearly mean from 2000 to 2020} & \multirow{3}{*}{GPP dynamics} \\  & (500 m, Daily) & & \\ \hline \multirow{3}{*}{VT} & PML\_V2 & \multirow{3}{*}{Yearly mean from 2000 to 2020} & \multirow{3}{*}{VT dynamics} \\  & (500 m, Daily) & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Details of multi-source and multi-sensor satellite data used in the analysis.  
Figure 1: Location map of the study area Rajmahal Hills in Jharkhand, India. The false color composite (FCC) is derived using near-infrared, red, and green bands of Landsat-8 satellite data. The region of interest is highlighted using blue color inside FCC.