esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Linear Model for the Calculation of Routing Metrics in 802.11s Using ns-3 and RStudio
## 2 Methodology  
The methodology is divided into three parts: in the first one, the ns-3 network simulator comprises the variable data from the IEEE 802.11s standard with other additional variables that will be addressed subsequently.  
The second part processes the gathered data in the RStudio software. In the last, statistical functions are employed to include a priori tools before returning to the ns-3 simulator to validate the suggested linear models.  
Finally, the ns-3 simulator is back to compare the resulting models in the RStudio with the original one, IEEE 802.11s.  
Figure 2 displays a detailed diagram of this last process.  
Furthermore, the TXX tool allows for knowing the network performance without use of the ns-3 simulator. By inserting the other variables, this tool predicts the network performance. This is possible with the analysis of variance (ANCOVA) technique. Figure 3 depicts a detailed model of the last process.  
The next section describes the suggested methodology in detail; this requires changes in the calculation code of the ALM metric as part of the HWMP application in ns-3. This process incorporates other network characteristics such as the layer 4 protocol, packet  
Figure 3: Methodology for predicting network performance.  
Figure 2: Methodology for computing the performance of the wireless mesh network (WMN) using the new linear model.  
numbers, mesh size, etc. These resulting data are stored in a compatible file and then transported to an RStudio for a statistical evaluation of the initial model, and therefore a proposal for new linear models using \(lm\) and \(glm\), and finally for the calculation of the ALM metric.
# Solar Ultraviolet Irradiance Characterization under All Sky Conditions in Burgos, Spain
## 2 Experimental Data
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sky Type** & **Parameter** & **All Data** & \multicolumn{4}{c}{**Season**} \\ \cline{4-7}  & N & 34,270 & 7,824 & 10,458 & 6,972 & 9,016 \\  & _GHI_ (W\(\cdot\)m\({}^{-2}\)) & 353.27 & 258.17 & 412.21 & 499.63 & 254.23 \\ All Sky & _GHUV_ (W\(\cdot\)m\({}^{-2}\)) & 13.33 & 9.36 & 15.83 & 19.09 & 9.41 \\  & \(T\) (\({}^{\circ}\)C) & 13.20 & 7.44 & 13.68 & 21.51 & 11.21 \\  & _RH_ (\%) & 67.96 & 75.31 & 65.40 & 54.82 & 74.72 \\  & WS (m\(\cdot\)s\({}^{-1}\)) & 2.15 & 2.37 & 2.17 & 1.97 & 2.09 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Seasonal 10 min mean values (N) between 14 September 2020 and 6 June 2022 for global solar irradiance (_GHI_), global ultraviolet irradiance (_GHIUV_), air temperature (\(T\)), relative humidity (\(RH\)), and wind speed (\(WS\)), for overcast, intermediate, and clear sky conditions classified in accordance with the \(k_{t}\) criteria.
# Nutritional Factors Associated with Dental Caries across the Lifespan: A Review
## 8 Discussion
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Age Group**} & **Nutritional Lifelong Profile** & \multirow{2}{*}{**Nutritionist Interventions**} & \multirow{2}{*}{**Educational Actions**} \\  & **for Oral Health** & & \\ \hline Infants (0–2) & \begin{tabular}{c} Breast milk/formula for \\ infants, introduction of solid \\ foods with guidance \\ \end{tabular} & \begin{tabular}{c} Nutrition counseling for \\ breastfeeding/formula feeding \\ \end{tabular} & \begin{tabular}{c} Oral hygiene education for \\ parents, early childhood feeding \\ \end{tabular} \\ \hline Children (3–12) & \begin{tabular}{c} Balanced diet with emphasis \\ on calcium-rich foods, limit \\ sugary snacks \\ \end{tabular} & Nutritional guidance for parents & \begin{tabular}{c} School programs on healthy \\ eating, importance of oral care \\ \end{tabular} \\ \hline Adolescents & \begin{tabular}{c} Increased calcium and \\ vitamin D intake, reduced \\ sugar consumption \\ \end{tabular} & \begin{tabular}{c} Individualized dietary plans, \\ addressing peer influences \\ \end{tabular} & \begin{tabular}{c} School-based workshops, \\ promoting healthy food choices \\ \end{tabular} \\ \hline Adults (18–50) & \begin{tabular}{c} Mediterranean balanced diet \\ and focus on fruits, \\ vegetables, and whole grains \\ \end{tabular} & \begin{tabular}{c} Personalized nutrition \\ consultations \\ \end{tabular} & \begin{tabular}{c} Workplace wellness programs, \\ seminars on nutrition and its \\ impact on oral health \\ \end{tabular} \\ \hline Older adults & \begin{tabular}{c} Adequate calcium and \\ vitamin B12, hydration, and \\ soft food options \\ \end{tabular} & \
\begin{tabular}{c} Nutrition support for age-related \\ oral health challenges \\ \end{tabular} &
\begin{tabular}{c} Community seminars on nutrition \\ for seniors, workshops on senior \\ nutrition and oral care \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of nutritionist interventions and educational actions for each age group. scores the necessity of advancing consultative techniques in nutrition education among various health professionals considering their unique effects on overall and dental health. Collaborative efforts, such as referrals from dentists to dietitians-nutritionists for individuals at high risk of dental caries and joint community health promotion initiatives involving dentists and dietitians-nutritionists, are encouraged. Training seminars for dentists and dietitians-nutritionists by food scientists can further enhance their ability to develop and promote informative food labeling for relevant nutrients, such as sugar and caffeine.
## 3 Preliminaries
### Fingerprint-Graph Transformation  
Many data-driven fingerprint-based methods utilize conventional machine learning techniques or deep learning models, which only work for Euclidean data and are ineffective for non-Euclidean data (e.g., fingerprints) [74]. In comparison, GNNs are particularly designed for non-Euclidean data and have proven to be efficient for various downstream tasks [75]. Hence, the fingerprints should be first transformed into graphs to adapt GNN-based models. In our prior work [74], we proposed a two-stage preprocessing method to achieve this.  
We consider an environment with \(|T|\) RF technologies, where \(T=\{t_{1},t_{2},\ldots\}\) denotes the set of all RF types. Then, as illustrated in Figure 6, the transformation method is as follows.  
Step 1. Abstraction:First, we gather information of all transmitters, including their types and locations. In the example shown in Figure 6, the transmitters are access points (APs), and \(T=\{t_{1},t_{2}\}\), which means there are two types of RF signals. We consider transmitters as vertices in a graph. The green vertex is the DOI, whose location is unknown. Then, we assign the vertex features for transmitters by the combination of their locations and RSSI. Note that vertices of different types should be considered per type, which results in _heterogeneous_ graphs.  
Different RF technologies usually have different propagation laws, which means that encoding with the same set of model parameters is insufficient. Next, for connectivity, we first make two assumptions to decide on the adjacency between vertices.  
Assumption I: Edges between vertices denote all possible signal propagations and interferences.  
Assumption II: A transmitter will only affect other transmitters of the same type.  
Step 2. Connection:Considering Assumption I, since the DOI measures RSSI from all transmitters, there must be edges between the DOI and all transmitters. Note that these edges are _unidirectional_ because the DOI is only a measuring device. Assumption II implies that the RSSI from a transmitter measured by the DOI is a combined result of all transmitters of the same type. Hence, transmitters of the same type will be fully connected. Since transmitters of the same type will affect each other, the edges within each _sub_-graph are _bidirectional_.  
We can also assign edge features by theoretical signal attenuation models. In [74], the log-normal shadowing model (LNSM) was adopted [76]. In this way, given an arbitrary fingerprint, this transformation method could generate a corresponding graph for GNN models. In Section 4.3, we modify this preprocessing method to generate graphs for fingerprints gathered in RIS-enhanced environments.  
Figure 5: Path loss model in RIS-enhanced environments where the channel matrices \(\mathbf{H}\) among the BS, the DOI, and the RIS are known.
## 3 Methodology
### Signal Recognition and Classification Based on DenseNet
The nonlinear transformation equation of ResNet is as follows:  
\[x_{l}=H_{l}(x_{l-1})+x_{l-1}\]  
The nonlinear transformation equation of DenseNet is as follows:  
\[x_{l}=H_{l}([x_{0},x_{1},\dots x_{l-1}])\]  
The dense connection mode of this model has many advantages. It increases the transmission of gradient, reuses features, and even reduces overfitting on small sample data. At the same time, DenseNet has fewer parameters than ResNet, less computation and higher accuracy. The Dense block is a basic module of DenseNet, and DenseNet is divided into several Dense blocks. In each node of the Dense block, the input is concatenated feature maps, and the size of the feature maps within each Dense block is the same. A Transition module is used to perform down-sampling transition connections between each Dense block. The nonlinear combination function in the Dense block refers to the combination of BN + ReLU + Conv. The simple model structure of the Dense block is shown in Figure 10.  
Figure 11 shows a complete DenseNet structure, which includes 3 Dense Blocks and 2 Transition layers. The Transition layers connect each Dense Block and consist of convolution and pooling to downsample and compress the model. After completing these steps, the data will enter the prediction or classification module for processing. Each layer of DenseNet is designed to be very narrow to reduce redundancy. Instead of drawing representational power from extremely deep or wide architectures, DenseNet exploits the potential of the network through feature reuse, yielding condensed models that are easy to train and highly parameter-efficient. Concatenating feature-maps learned by different layers increases variation in the input of subsequent layers and improves efficiency. The network improves the flow of information and gradient, making it easy to train, and the intensive connection has regularization benefits, reducing the overfitting problem of a small training set. Additionally, after verification, the model has a good perception effect on the defect signal in this experiment and a strong feature extraction ability. Figure 10: The simple schematic diagram of a Dense block.
## 4 Study Case
### Data Analysis: Iteration 0
#### 4.3.1 Keywording  
**Keywording** is a crucial component of the GSMS methodology, which involves the constant comparison of data and abstractions through open, selective, and theoretical coding that are associated with concepts, categories, and propositions, respectively [14].  
Figure 3: Activity diagram for iteration 0.  
The goal of GSMS keywording is to develop a classification schema based on the previous knowledge in the literature. Figure 4 shows the saturation search process.  
In **open coding**, two concepts were identified: software development and GT. These concepts have been extensively studied in the scientific literature, and they were compiled in two lists of relevant terms: one for [Software Development], obtained from Swebok v3.0 [13], and the other for [Grounded Theory variants] [5,8,9].  
In **selective coding**, categories were established based on these lists. The **{Grounded Theory list}**[5,8,9] had two variants: [Glaserian Grounded] and [Straussian Grounded]. The **{Software Development list}**[13] included ten categories that are the body of knowledge for software development: 1. [Software Requirements], 2. [Software Design], 3. [Software Construction], 4. [Software Testing], 5. [Software Maintenance], 6. [Software Configuration Management], 7. [Software Engineering Process], 8. [Software Engineering Models and Methods], 9. [Software Quality], and 10. [Software Engineering Management].  
**Theoretical coding** seeks to establish propositions, which are abstract and deeper levels of the categories obtained in selective coding, and they encompass several concepts. These propositions were refined and evolved into specific ways about software development and GT, and were unified into what is called the **[Pre-classification schema]**.  
**Keywording** aims to find a classification schema in an SMS. On the other hand, the [Pre-classification schema] is defined as arising from previous knowledge for the current iteration and refers to the two lists related to the knowledge available about software development and grounded theory.  
Figure 4: Saturation search within keywording in iteration 0.
# Performance of BDS-3: Measurement Quality Analysis, Precise Orbit and Clock Determination
## 3 Results
### Measurement Quality Analysis
#### 3.1.2 Multipath Combination
It becomes obvious that the multipath and noise for the B1 signal are higher compared to B3, which exhibits a standard deviation of about 0.5 m at low elevations angles and about 0.1 m at high elevation angles. Moreover, the standard deviations of B1 signal for BDS-3 are, on whole, slightly smaller than BDS-2 for the same satellite type at these two sites. Most important of all, the results of Figures 4-6 indicate that the new-generation BeiDou satellites have improved satellite-internal hardware device design to avoid the problem of code biases like for BDS-2, which would provide better performance. Figure 4: MP combinations of BDS-2 (red) and BDS-3 (blue) B1 frequency as a function of time and a function of elevation angle for IGSO **(a)**, and MEO **(b)** satellites at station BJF1. The olive lines represent corresponding satellite elevation. Figure 5: MP combinations of BDS-2 (red) and BDS-3 (blue) B3 frequency as a function of time and a function of elevation angle for **(a)** IGSO, and **(b)** MEO satellites at station BJF1. Figure 6: Mean and standard deviation (std) of MP combinations as a function of elevation for BDS-2 and BDS-3 IGSO and MEO satellites signals at stations BJF1 (left) and KUNU (right) during DOY 204-221, 2016. The upper and bottom panels show the results for B1 and B3, respectively. I(2) and M(2) represent BDS-2 IGSO (C06, C07, C08, C09, C10) and MEO (C11, C12, C14) satellites, and I(3) and M(3) represent BDS-3 IGSO (C31, C32) and MEO (C33, C34) satellites.
## 3 Materials and Methods
### Phytochemical Investigations  
#### 3.3.1 Identification of Phenolic Compounds by HPLC-PDA/ESI-MS  
The analyses were carried out using a Shimadzu HPLC system (Milan, Italy) equipped with a CBM-20A controller, LC-20 AD pumps, a DGU-20A3 degasser, a SIL-20 AC autosampler, an SPD-M20A photodiode array detector (PDA), and a triple quadrupole mass analyzer (LCMS-8050, Shimadzu, Kyoto, Japan), equipped with an ESI interface, in positive and negative ionization modes. Data acquisition was performed by Shimadzu LabSolutions software ver. 5.91.  
_Samples and sample preparation_: An amount of 20 mg of _M. frucitulosa_ subsp. _fruticulosa_ and subsp. _coronopifolia_ extracts was dissolved in 1 mL of MeOH.  
_Chromotagraphic conditions_: Analyses were carried out on an Ascentis Express C18, 15 cm \(\times\) 4.6 mm LD. with a particle size of 2.7 \(\upmu\)m (Merck Life Science, Merck KGaA, Darmstadt, Germany). The injection volume was 5 \(\upmu\)L, the mobile phase consisted of water/formic acid (99.90.1, \(v/v\)) (solvent A) and (formic acid (99.9:0.1, \(v/v\)) (solvent B), and the linear gradient profile was as follows: 0 min, 0% B; 5 min, 5% B; 15 min, 10% B; 30 min, 20% B; 60 min, 50% B; 70 min, 100% B; 71 min, 0% B. The flow rate was 1 mL/min, and it was split to 0.2 mL/min prior to MS detection.  
_PDA conditions:_ The wavelength range was 200-400 nm, and the chromatograms were extracted at a wavelength of 280 nm. Time constant was 0.08 s, and the sample frequency was 40 Hz.  
_MS conditions:_ The mass spectral range was 100-1000 \(m/z\), the interval was 0.5 s, the nebulizing gas (N\({}_{2}\)) flow was 1.5 L/min, the interface temperature was 350 \({}^{\circ}\)C, the heat block was 300 \({}^{\circ}\)C, the DL temperature was 300 \({}^{\circ}\)C, the DL voltage was \(-\)34 V, the probe voltage was 4.5 kV, the Qarray voltage was 1.0 V, the RF voltage was 90 V, and the detection gain was 1.0 kV.  
Quantitative determination was performed using the calibration curves of six standards, namely, gallic acid, hydroxybenzoic acid, chlorogenic acid, luteolin, kaempfer,and quercetin. Standard calibration curves were prepared in the concentration range of 0.1-50 mg/L with five different concentration levels [27].
## 3 System Implementation and Performance Evaluation
### Multi-System Compatible Solution  
Hive requires relatively few memory resources, but it will write temporary files to HDFS frequently, resulting in long computation times and poor performance. In contrast, SparkSQL needs much more memory because most of the operations are done in memory and uses less write files to hard disk. SparkSQL, however, may risk a routine crash as the memory is not sufficient. On the other hand, Impala supports most standard SQL statements better for more advanced SQL query requirements in business intelligence. Since each tool has their own specialized features, people can only choose a suitable analytics tool for the specific data size or type. Therefore, the use of a multi-system compatible solution is proposed in this paper, as shown in Figure 12, and is able to balance the strengths and weaknesses of the above tools. With small-scale data and real-time requirements, SparkSQL is the best choice, while large-scale data and batch processing requirements will best be met using Apache Hive. If business intelligence or analytical tools have more advanced SQL Query requests, Impala will support most standard SQL statements. Thus, this study will also design a tool selection for this feature. Once the tool selection has received the SQL command, it starts to check the cluster status [25] and the current memory usage among computing nodes, i.e., virtual machines [26]. After that, the tool selection chooses the appropriate tool--Hive, Impala, or SparkSQL--to execute the SQL command.  
Figure 11: The average execution time of one record by SQL command V.
# Development and Application of a Novel Tsunami Monitoring System Based on Submerged Mooring
## 3 Key Technologies
### High-Reliability Data Transmission Design
#### 3.2.1 Inductively Coupled Transmission Design  
Commonly used underwater data transmission technologies currently include cable transmission and wireless transmission. Cable transmission often uses multi-core watertight cables to connect sensors for communication. However, tsunami monitoring systems are located in deep water areas with long anchoring structures, which have the disadvantages of signal strength attenuation with distance and cable damage; wireless transmission technology includes laser communication, underwater acoustic communication, and inductive coupling communication, of which laser communication has strong directionality [23; 24]. Factors such as water quality and plankton often affect communication effectiveness, limiting its application range [25; 26]; underwater acoustic communication utilizes sound waves for underwater communication. The fluctuation and multipath effects of the underwater acoustic channel result in a low propagation rate, large delay, high bit error rate, and high power consumption of transceivers, which is not conducive to the long-term operation of tsunami monitoring systems. Electromagnetic induction coupling communication technology relies on small-sized coupling coils to induce magnetic field components for communication. Its magnetic induction signal propagates at the speed of light in the transmission medium, which has the advantages of a small transmission delay, stable channel, and high reliability and has good application prospects [27; 28]. This article uses electromagnetic induction coupling technology for underwater transmission to achieve real-time transmission of pressure sensor data to the data collector, improving the reliability of data transmission. A schematic diagram of induction coupling transmission is shown in Figure 4.  
Figure 4: Schematic diagram of inductive coupling transmission.
## 2 Theoretical Background
### Communication Layer
#### 2.5.2 False Data Injection (FDI) Attack Simulation
The values for the transmission delay and inter-arrival time fall within close ranges of each other for normal traffic measurement in the figures. Figure 5a,b illustrate the transmission delay and inter-arrival time of 21,600 samples for the 690 th malicious traffic measurement. An instance of an FDI attack is demonstrated with a higher transmission delay at a maximum value of 8.30 in the 7712 th sample, indicating malicious traffic. An occurrence of malicious traffic is represented in the 7718 th sample, which shows an increased inter-arrival time with a maximum value of 3.83.
# Sink-Type-Dependent Data-Gathering Frameworks in Wireless Sensor Networks: A Comparative Study
## 4 Quantitative Performance Comparison via Simulation
### Delay Model
It should be noted that the generated packet numbers for three of them were equal, as were the aggregation percentages. The data is collected from the first ten rounds of transmission for five times, and the average result for a single round is shown in the graph. The reason behind the high number of packet drops in the IoJHR protocol is its unsynchronized sink mobility, i.e., its sink path is predefined. The CHs calculate the presence of a sink based on predefined information. Any fluctuation in the sink timing will cause a bad result. NDCMC uses beaconing techniques to initiate data transmission, resulting in a lower number of packet drops compared to the IoJHR protocol. However, after the death of the CH, the nodes send data directly to the aerial sink, the transmission distance being higher which causes packet drops. As the nodes only have a small window for transmitting data, no speed optimization is performed based on the volume of transmission data. It should be noted that all the nodes have an equal transmission range; therefore, for the equal speed of a sink, the CHs will have an equal-sized window in which to transmit data. Thus, a small degree of asynchronization, with the higher volumes of data, results in poorer performance. For the HEED protocol, the sink is always present in the ROI, and the distance from the CHs is optimal. However, the CH-to-sink transmission distance makes for a long transmission time. This results in a lower number of packet drops.