esa-sceva/satcom-chunk-collection huggingface dataset:
# Internet of Everything (IoE) Taxonomies: A Survey and a Novel Knowledge-Based Taxonomy
## 4 Proposed IoE Taxonomy
### Knowledge
#### 4.1.2 Structure  
The combination of data streams with background knowledge enables meaningful analysis to derive higher levels of abstraction and deliver quality actionable information to IoE services [71; 95; 99]. Sensor data are a piece of explicit knowledge with metadata characterizing the body of evidence [68]. The distinctions between data, information, and knowledge are largely irrelevant [97]. Knowledge is created by transforming the multiple data formats collected (structured, semi-structured, and unstructured) [103] into high-level information [36; 64; 94; 104], and useful knowledge patterns [36]. Descriptions of these data formats are given below:  
* Structured: These data have an identified format and a relational structure, frequently accessed using a standard SQL-type language and stored in relational database management systems. Typical examples of structured data are string, numeral, and date. [105].
* Semi-structured: These data cannot be managed by conventional database management system techniques, but the interpretation and analysis of these data require comprehensive and intelligent rules. Typical examples of semi-structured data are extensible markup language (XML) and JavaScript object notation (JSON) data. [50; 101; 105].
* Unstructured: These data do not follow any specific format and are often represented in a rather complex structure that contains hidden relationships. Examples of unstructured data are videos, text, time information, and geographic location [40]. With the amount of data generated by sensors, devices constantly produce large volumes of structured, unstructured, and semi-structured data, which results in \"big data\" [73; 74].  
IoT processing of sensing data streams provides ubiquitous sensing services [42; 102; 106]. Data aggregation processes are vital for improving the quality of the designed system [107]. Big data technologies assist in data processing [76], the uncovering of new and valuable insights and information from incorporated data sources [28; 69], and in improving prediction and decision-making [102].
## 3 System Model
### Scientific Workflow Application Model
Also, each edge \(e_{ij}\) has a communication time \(CT_{ij}\) which denotes the amount of data needed to be communicated and the precedence constraint from tasks \(v_{i}\) and \(v_{j}\), if they are executed on different resources. It is predetermined and known a priori. In this case, task \(v_{i}\) is considered one of the immediate predecessor of task \(v_{j}\) and task \(v_{j}\) is considered one of the immediate successor of task \(v_{i}\). The predecessor task of \(v_{i}\) is denoted as \(pred(v_{i})\) and the successor task as \(succ(v_{i})\). Task \(v_{i}\) can have multiple predecessors and multiple successor tasks. A task is considered as a ready task when all its predecessors have finished execution. Any task without a predecessor task is called as the entry task \(v_{entry}\) and a task with no successor task is called the exit task \(v_{exit}\). Since the proposed method needs a graph with individual entry and exit nodes, virtual entry and exit nodes denoted by \(v_{entry}\) and \(v_{exit}\) respectively, with zero execution time and zero transmission time have been inserted into the DAG. The crux of the proposed research is scheduling a BoT-based workflow based on the deadline constraint while minimizing the budget of the application. The proposed approach, called DSB, considers pay-as-you-go IaaS cloud model having inherent features such as elasticity, abundance, heterogeneity, dynamic provision of resources, interval-based pricing model and VM provisioning delays. A user-defined deadline is submitted initially with the workflow DAG.
## 4 Discussion
### Input Variables Concurrent Validity  
The input variables (V\({}_{\text{max}}\) and \(\tau\)) are directly related to the model's output variables, which explains the observed similar levels of correlation [22] (Figure 6). Variables related to the constant speed phase (V\({}_{\text{max}}\) and V\({}_{0}\)) determined by GPS and radar are very strongly correlated, whereas less significant correlations were obtained for parameters related to the acceleration phase (F\({}_{0}\) and \(\tau\)). These results are concordant with a study conducted by [PERSON] et al. in 2014 [23] and also with a study conducted by [PERSON] et al. in 2022 [13], which showed a good GPS accuracy at constant speed, but a poor accuracy during acceleration phases.
## 2 Partial Discharge Detection Using the AE Method and Signal Processing Analysis
### Experimental Setup  
The MFC-LDT1-028K and PZT-7 BB-35-3 sensors were fixed to a three-phase distribution transformer wall (Figure 2) using lithium grease gel to capture the AE signals emitted by PDs. Figure 3 shows two copper electrodes placed in front of each other with a 1 mm gap between them to form a needle-needle configuration (PD source). The PD source was placed inside the transformer, and a PD was generated by applying 9 kV between the electrodes with a high-voltage transformer. The acoustic signals sensed by the piezoelectric sensors (Figure 4) were transferred to an oscilloscope via twisted pair cables to avoid EMI and other environmental noise. An oscilloscope captured the data, the acquisition rate was set at 500 MSa/s, and no filter or amplifier was used in the circuit. As discussed in the next sections, several routines for signal processing analysis were developed in MATLAB.  
Figure 1: Acoustic emission sensors (**a**) PZT-7 BB-35-3 and (**b**) MFC-LDT1-028K used in the experiment.
# Towards Secure Searchable Electronic Health Records Using Consortium Blockchain
## 5 Discussion
### Our Protocol Ensures EHR Sharing Integrity and Confidentiality  
Before sending EHRs to a private blockchain, the doctor encrypts it with the patient's public key, the doctor's public key, and a set of keywords retrieved from the EHR. As a result, without the patient's private key and keywords, the ciphertext cannot be decrypted.  
Figure 8: Encryption algorithms’ consuming gas in _eth_ with different key size. We vary the key size (256 bits, 384 bits, and 512 bits) and measure the consumed gas for uploading the record to the blockchain. For each key size, we conduct the experiment 5 times and next take the average.  
Figure 7: Encryption algorithms’ consuming gas in _eth_ with different records sizes. We vary the record size (16 bits, 64 bits, 128 bits, 256 bits, and 512 bits) and measure the consumed gas for uploading the record to the blockchain. For each record size, we conduct the experiment 5 times and next take the average.  
Under the ECDH assumption, the private key is secure. Furthermore, only the institution authorized by the patient has access to the data on the private blockchain. In fact, the system manager uses the public key and keyword of the doctor to construct a re-encryption key. As a result, the ciphertext can only be decrypted by the intended third party, enhancing data confidentiality. Furthermore, each block's signatures ensure data integrity.
## 5 Fragility and Leakage Risk Assessment of the NCS
### Leakage Risk Assessment  
#### 5.3.1 Probabilistic Safety Margin  
The probabilistic safety margin reflects the capacity of the NCS to withstand beyond-design-basis accidents, and thus it is usually used as an important index in the probabilistic  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\eta\) & \(P_{\text{m,5\%}}\) & \(P_{\text{m,50\%}}\) & \(P_{\text{m,95\%}}\) & **HCLPF** & **Reduction Rate of HCLPF** \\ (\%) & (MPa) & (MPa) & (MPa) & (MPa) & (\%) \\ \hline
0 & 2.004 & 1.645 & 1.35 & 1.302 & 0 \\
10 & 1.889 & 1.551 & 1.273 & 1.214 & 6.79 \\
20 & 1.779 & 1.46 & 1.199 & 1.135 & 12.83 \\
30 & 1.692 & 1.389 & 1.114 & 1.076 & 17.35 \\ \hline \hline \end{tabular} Note: \(P_{\text{m,gs,}}P_{\text{m,95\%}}\), and \(P_{\text{m,95\%}}\), represent the median pressure capacity of the fragility curve with 5%, 50%, and 95% confidence, respectively.  
\end{table}
Table 4: Pressure capacity of the NCS with different confidence levels.  
Figure 14: Fragility curves of the NCS at different degrees of liner corrosion.  
performance assessment of the NCS. The probabilistic safety margin of the NCS with consideration of the confidence level can be calculated as follows:  
\[S_{\text{psm},\omega}=P_{\text{m}}\cdot\exp\Bigl{(}-\Phi^{-1}(\omega)\cdot\beta_{ M}\Bigr{)}/p_{\text{d}} \tag{14}\]  
where \(p_{\text{d}}\) is the design pressure of the NCS, and the design pressure for the investigated NCS is 0.42 MPa.  
Figure 15 shows the probabilistic safety margins of the NCS at different degrees of corrosion liner. It can be found that the probabilistic safety margin gradually decreases as the corrosion degree and confidence level increase. When the corrosion degree reaches 30%, the probabilistic safety margin with a 95% confidence level drops to 2.71. Within the scope of this paper, the NCS under liner corrosion conditions can satisfy a safety margin of not less than 2.5 [18].
# Research on Tbps and Kilometer-Range Transmission of Terahertz Signals
## 4 High-Sensitivity SIMO Space-Ground Communication Architecture
### Simulation Results and Discussion  
This Earth-satellite communication simulation under the SIMO architecture successfully modeled the dynamic satellite-ground station distance variation over 40 min caused by Earth's revolution and rotation, along with its impact on communication performance, as shown in Figure 16a,b. The distance increased from 3.79999 \(\times\) 10\({}^{7}\) m to 3.80007 \(\times\) 10\({}^{7}\) m, exhibiting a significant decline in \(\mathrm{P}_{\mathrm{r}}\) with increasing range.  
As shown in Figure 16, the simulation results robustly validate the effectiveness of the core implemented technologies: DSP and MRC diversity reception techniques substantially enhanced communication quality. At the receiver, the constellation diagram effectively restored phase alignment after DSP processing and demonstrated sharper, more distinct clustering characteristics following MRC combining. The BER prior to diversity reception (Figure 16c) fluctuated around the 10\({}^{-2}\) level, while post MRC combining (Figure 16d), it significantly reduced to the 10\({}^{-3}\) order of magnitude. Concurrently, a system gain of approximately 4 dB in SNR was achieved.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Frequency & 300 GHz \\ Distance & 36,000 km \\ Symbol Rate & 25 GBaud \\ Symbol Length & 32,768 \\ Sampling Points per Symbol & 30 \\ Modulation Order & 16 \\ TX Antenna Diameter & 4.5 m \\ RX Antenna Diameter & 2.06 m \\ Aperture Efficiency & 0.6 \\ Number of RX Antennas & 4 \\ Transmit Power & 1 W \\ \hline \hline \end{tabular}
\end{table}
Table 1: High-sensitivity SIMO communication architecture system parameters.  
Figure 15: SIMO satellite–ground communication flowchart.  
Addressing weather-induced attenuation in the satellite-to-Earth link, the simulation contrasted performance under clear sky, fog attenuation, and rain attenuation conditions. Under fog attenuation (Figure 16h), SNR fluctuations remained below 0.1 dB compared to the clear sky baseline (Figure 16g). Rain attenuation caused more noticeable SNR degradation, though the maximum attenuation was controlled at approximately 1 dB.  
This simulation not only demonstrates the technical feasibility of utilizing the terahertz band for Earth-satellite communications under dynamic link conditions and typical weather scenarios but also quantitatively proves the critical role of DSP and diversity reception techniques in enhancing link robustness and reliability.
# Marine Bioactives and Their Application in the Food Industry: A Review
## 2 Marine Sources of Bioactive Molecules
### Marine Cyanobacteria
#### 2.1.3 Fatty Acids from Microbiota  
Marine microorganisms such as bacteria, protists, yeast, dinoflagellates, and filamentous fungi have been identified as producers of long-chain PUFAs in the marine environment. Moreover, numerous bacterial species (e.g., _Vibrio marinus_, _Moritella marina_, and _Shewanella marinittestina_) of marine origin have been found to have long-chain PUFAs, especially the omega-3 and omega-6 series of fatty acids, including eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA). In particular, EPA production is mainly observed in fungi and microalgae [43]. In addition, microalgae, including Spirulina, provide an interesting source of fatty acids, including lauric, palmitic, oleic, and gamma-linolenic acids with the omega-3 fatty acid DHA, and it has been reported to contain sterols, including clionasterol [44]. The content of PUFAs in marine microalgae is higher than that of freshwater microalgal species, as marine microalgae deliver higher amounts of PUFAs to survive in extreme marine environments. Furthermore, microalgae are also able to alter PUFA production rates with the growth phases. _Nanochloropsis spp._, _Phaodactylum tricornutum_, _Porphyridium cruentum_, and _Chaetoceros calcitrans_ have been reported as the best EPA-producing microalgae, while _Cryptecodinium_ spp. and _Isochrysis galhana_ are more suitable for extracting DHA [45].  
As conventional sources of PUFAs, fish oils encounter several natural constraints and limitations, such as producing undesirable odors and suffering from low stability. Furthermore, the purification of PUFAs (e.g., DHA) from fish sources is relatively expensive due to technical difficulties [46]. In contrast, microbial PUFA production is sustainable and independent of climatic or seasonal changes as marine microorganisms, including microalgae and fungi, are able to grow on a diverse range of substrates with relatively rapid growth rates. For instance, microalgae, such as _Amphidinium_ sp., contain up to 48% of their cell fatty acids as DHA [47]. Various yeast and fungal species have also been identified as potential alternative sources to produce long-chain PUFAs. For example, _Cumningham echinulate_, _Mucor circinelloides_ strainsa, _Thamidium_ elegans strains, _Yarrowia lipolytica_, and _Zygorhynchus moelleri_ have been reported as PUFA-producing fungi [46]. Various microbial oils are already commercially produced and can be manipulated by maintaining culture conditions, such as providing unique substrates for microbial enzymes and genetic engineering to enhance microbial lipid-producing biosynthetic pathways. Large-scale production of PUFA-rich microbial oils has already been popular in the industrial world with genetically modified strains [46; 48].
# Analysis of the Water Quality Improvement in Urban Stream Using MIKE 21 FM
## 5 Model Verification and Analysis Results
### Model Simulation Results
#### 5.3.1 Comparison of BOD before and after the Supply of the Ozone Treated Reclaimed Wastewater  
Figure 12 presents the BOD concentration change at the analysis spots. Except for the Gulpocheon upstream, where there is no reclaimed water supply, the midstream and downstream were analyzed. Based on the analysis results, the BOD concentration decreased by 0.73 mg/L after the reclaimed water was supplied. Compared with the stream water, the BOD concentration of the Gulpo treated sewage water and ozone-treated water was lower; thus, the same tendency was found in the analysis spots within Gulpocheon.  
Figure 11: Scatter graph comparison.  
3.2 Comparison of Dissolved Oxygen (DO) before and after the Supply of the Ozone Treated Reclaimed Wastewater  
Figure 13 shows the DO concentration change at the analysis spots. As presented in Figure 13b-d, the DO concentration increased in all regions after the discharge of the ozone-treated reclaimed water. The DO concentration was increased by 3 mg/L and 1.5 mg/L midstream and downstream, respectively. It was improved by 1.1 mg/L on the whole, indicating water quality improvement.  
3.3 Comparison of Total Nitrogen (TN) before and after the Supply of the Ozone Treated Reclaimed Wastewater  
Figure 14 presents the TN concentration change at the analysis spots. As shown in Figure 14b-d, the TN concentration increased in all regions after discharge of the ozone-treated reclaimed water except the Gulpocheon upstream. This is because the nitrogen concentration of the treated sewage water and ozone-treated reclaimed water was higher  
Figure 12: Comparison of the BOD change at analysis spots.  
Figure 13: Comparison of the DO change at analysis spots.  
than that of the stream water. This difference was more evident at the midstream with relatively lower flow. Furthermore, the TN concentration increased downstream.
# Seismic Hazard Evaluation and Strain Dynamics in the
## 3 Methodology and Implementation
### Gutenberg-Richter Law
#### 3.2.2 Earthquake Recurrence Periods  
One of the most essential parameters for seismic hazard analysis is the earthquake recurrence period of active faults. The earthquake recurrence period refers to the frequency at which earthquakes occur on a fault or fault zone. These periods are particularly necessary for seismic risk analysis studies to be conducted in the areas affected by faults that have reached their recurrence period. The earthquake recurrence period can be determined through paleoseismological studies, fault parameters, and statistical methods. One of the methods used to calculate this period is the Gutenberg-Richter law. By using the \(a\)- and \(b\)-values estimated with the relation provided in Equation (1), the periods can be calculated using the relation in Equation (2). Here, T represents the recurrence period and N(M) refers to the annual average number of earthquakes with magnitude M or greater in this equation.  
\[T=\frac{1}{N(M)} \tag{2}\]  
As mentioned above, after calculating the \(a\)- and \(b\)-values using the Gutenberg-Richter law, the recurrence periods of earthquakes between M = 5 and M = 7.1 with intervals of 0.1 were computed for the study area using Equation (2) (Figure 6). There are several reasons for calculating the recurrence periods of earthquakes with an incremental 0.1 magnitude. Although it is stated in the literature that M > 6 earthquakes are generally capable of generating surface ruptures [63], the destructiveness of an earthquake is not entirely limited to its magnitude. The depth of the earthquake, underlying soil properties, proximity to populated areas, and the durability of structures (such as buildings, bridges, etc.) in the region also play a significant role in determining the destructive impact. The fact that the study area covers a large region and contains old settlements (such as buildings, houses, barns, schools, etc.) also increases the significance of the analysis conducted in this study [115]. There are also notable differences in the potential energy released by the earthquakes with magnitudes determined at incremental 0.1 magnitude steps. The results  
Figure 5: Estimated \(a\)- and \(b\)-values and standard deviations for the study area according to the Gutenberg–Richter law.  
indicate that the recurrence periods of earthquakes with magnitudes between M = 5 and M = 7.1 in the region range from 32 years to 982 years.
## 3 Our Implementation
### Filters
The collisions are marked red. Let \(c\) be a number of cells in scanned space and \(c_{max}\) is the maximum possible number of scanned cells, that could lie in this space, then the information gain \(I_{S}\) is denoted as a ratio between \(c\) and \(c_{max}\):  
\[I_{S}=\frac{c}{c_{max}} \tag{6}\]  
(4) Next Best View selection  
The next phase of the NBV method is to sort the filtered set of candidates according to a fitness function. The fitness function considers the information gain while minimizing the distance traveled by the robot and the rotations of the camera. Let us define the fitness function of the distance traveled as follows:  
\[D=1-\frac{||\overrightarrow{p_{c}},\overrightarrow{p^{\prime}}||}{2d_{max}} \tag{7}\]  
where \(||\overrightarrow{p_{c}},\overrightarrow{p^{\prime}}||\) is the Euclidean distance in the robot's frame between the current position of the camera \(\overrightarrow{p_{c}}\) and the position of the camera in the candidate \(\overrightarrow{p^{\prime}}\). The constant \(d_{max}\) represents the maximum range of the robot, which must be multiplied by two, since the candidate's position can be in the interval from \(-d_{max}\) to \(d_{max}\). The value of the fitness function is greater if the Euclidean distance is smaller. For the rotation fitness function, we will write the following:  
\[R=1-\frac{\Delta r}{\pi} \tag{8}\]  
where \(\Delta r\) denotes the difference in orientation between the current orientation and the orientation in the candidate. Again, the smaller the change in orientation, the greater the fitness function value. The best candidate is the one with the greatest value of the fitness function, which is defined as the combination of the Equations (6)-(8):  
\[f(p)=Dw_{d}+Rw_{r}+I_{S}w_{i} \tag{9}\]
# Predicting User's Measurements without Manual Measuring: A Case on Sports Garment Applications
### Regression Models  
In general terms, regression analysis searches the best matching relationship between given inputs and a desired output. This relation can, depending on the method, be linear in nature or computationally more complex. In this research, regression will be used to model the relation between straightforward demographics, e.g., stature, weight, age, and gender, and a set of output measurements that are used in pattern gradings to determine a subject's clothing size, e.g., chest circumference and hip circumference. In comparison to the shape models, the dimension of the input and output spaces is small compared to the thousands of data points that are used to represent a shape model. This reduces the size of computational data, and thus, adds the possibility to make the regression nonlinear, even with relatively few computational resources.
# Snow Depth Variations in Svalbard Derived from GNSS Interferometric Reflectometry
## 2 Data and Methods
### Method  
#### 2.2.1 Approach Based on LSP Spectral Analysis  
Note that the signal-to-noise ratio, after removing the trend term, is a sine-like function that oscillates with the change in satellite elevation. The oscillation frequency can be obtained by differentiating the change in satellite elevation with time. Because the frequency characteristics contain height information, we can derive the snow depth estimation from the height of the antenna phase center relative to the reflector. In this paper, SNR observation data with a satellite elevation of 5\({}^{\circ}\)-25\({}^{\circ}\) were selected for further processing and analysis. Since the SNR sequence is not sampled at equal intervals, LSP spectral analysis is often used to process the non-uniformly sampled signal-to-noise ratio sequences after trend removal to obtain the main frequency [26; 27]. Due to the influence of thermal and environmental noise, on some occasions, it is difficult to obtain the main frequency of the signal-to-noise ratio sequences via LSP spectral analysis. Figure 2 shows the spectral results of the de-trended SNR sequence for GPS pseudo-random number (PRN) 19 on February 28, 2018. This is a case that illustrates the failure of LSP in obtaining the main frequency, because two peaks can be found, which correspond to two main frequencies and can cause an error in snow depth estimation. Since it  
Figure 1: (**a**) Map of location of Ny-Alesund in Svalbard. (**b**) Picture of local environment surrounding GNSS station, photographed by the authors in field work in 2018.  
is particularly important to control the quality of the GNSS observations in snow depth estimation, an improved approach is proposed in the next section.