esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Polyphenol, Flavonoids and Condensed Tannins Content  
The polyphenols represent the major group of antioxidants of plant materials [50; 51]. They include a wide variety of bioactive metabolites that contain at least one aromatic-hydroxyl group in addition to other substituents with large spectra of biological activities [45]. They are divided into different groups such as flavonoids, tannins and phenolic acids [45]. In this study, the amount of total phenolics, flavonoids and condensed tannins varied in the three beverages (Table 2). A high level of polyphenols, with values of 315.6;  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Chemical Parameters**} & \multicolumn{2}{c}{**Batch**} \\ \hline  & **FB-lc** & **FB-sp** & **FB-lcsp** \\ \hline Total sugars (mg/L) & 23.7 \(\pm\) 2.2 & 22.1 \(\pm\) 3.3 & 20.3 \(\pm\) 0.5 \\ Reducing sugars (mg/L) & 12.5 \(\pm\) 1.4 & 10.3 \(\pm\) 0.8 & 9.5 \(\pm\) 0.4 \\ Total Protein (mg/L) & 17.8 \(\pm\) 0.5 & 16.63 \(\pm\)1.4 & 15.6 \(\pm\) 0.3 \\ Total phenolics (\(\mu\)g GAE/mL) * & 182.5 \(\pm\) 12 & 289.7 \(\pm\) 11 & 315.6 \(\pm\) 21 \\ Flavonoides (\(\mu\)g RE/mL) ** & 95.2 \(\pm\) 8.2 & 119.3 \(\pm\) 2 & 152.1 \(\pm\) 5.3 \\ Tannins (\(\mu\)g CE/mL) *** & 87.6 \(\pm\) 7.11 & 73.1 \(\pm\) 3 & 93.6 \(\pm\) 9.1 \\ \hline \hline \end{tabular}  
* FB-sp: obtained beverage fermented by _Biflabacterium brere B632_. FB-lcsp: the obtained beverage fermented by a mix of both strains. FB-lc: the obtained beverage fermented by _L. acidophilus_ ATCC 43121.
* ug of gallic acid equivalents per mL of beverages. *
* ug of trutin equivalent per mL of beverages. **
* ug of catechin equivalent per mL of beverages. Results are expressed as the mean of 3 replicates.  
\end{table}
Table 2: Total sugars and proteins, total phenolic compounds, flavonoids and tannins contents of obtained beverages.  
Figure 1: LAB cells count, expressed as log of colony forming unit/mL (**a**), pH and titratable acidity (**b**) of the three different products during the lactic acid fermentation process. Results are reported as mean values of 3 replicates.  
289.7 and 182.5 \(\upmu\)g GAE/mL, were observed for FB-lcsp, FB-sp and FB-lc, respectively. The highest content of flavonoids (152.1 \(\upmu\)g RE/mL) and tannins (93.6 \(\upmu\)g CE/mL) was found in the FB-lcsp trial. It is well known that the secondary metabolites possess a wide range of biochemical and pharmacological properties [48]. These results suggest that the mixed culture improves the extraction of secondary metabolites such as flavonoids, tannins and phenolic acids. During fermentation, many changes of composition occur, leading to a modified ratio of nutrients and anti-nutrients and, therefore, the properties of the product, such as bioactivity and digestibility, are modified [52]. Lactic fermentation of plants has been shown to increase the concentration of several phenolic compounds [53]. So, the richness of our fermented plant beverages with phenolic compounds, especially FB-lcsp, could be an important starting point to explore their biological activities.
# Implementation of Autonomous Mobile Robot in SmartFactory
## 2 Autonomous Mobile Robots  
This section describes the chosen autonomous mobile robot and its parameters. The selected mobile robots are developed by the MiR company; specifically, a MiR100 model was found for the demonstration tasks. This mobile robot is designed for transportation purposes and to automate logistics. Its designation is based on the weight it can transfer (100 kg). The model is equipped with advanced sensors, which means that the robot moves autonomously in the environment and responds to surrounding obstacles. Table 1 provides basic information about the model [19].  
The selected model is the smallest one and, thanks to its dimensions, it is possible to navigate this robot into the production line. The top of the robot is also supplemented by a superstructure, on which the products from the production line are placed. Figure 1 shows a photo of the mentioned mobile robot.  
All configurations and settings of the mobile robot (MiR, MiR FLEET, WISE digital input, and output cards) are performed via a Web interface. Each web interface is different according to the needs of a particular device but is very similar in style [10]. The navigation system is the core of the autonomous mobile robot. Its goal is to plan the route from starting point A to destination point B. These points are determined by the user, but the journey is already planned completely, autonomously, and automatically. An advanced navigation system, which consists of several parts, is responsible for determining the correct route and navigating the robot to the destination. Figure 2 shows the principle of system operation. For clarification, it should be noted that IMU is inertial measurement unit.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Property** & **Unit** & **Value** \\ \hline Model & — & MiR100 \\ Length & mm & 890 \\ Width & mm & 580 \\ Height & mm & 352 \\ Weight & kg & 70 \\ Max. load capacity & kg & 100 \\ Max. forward speed & m/s & 2 \\ Max. backward speed & m/s & 1.5 \\ Turning radius & mm & 520 \\ Positioning accuracy & mm & \(\pm\)50 \\ Operating time & hours & 10 \\ Ambient temp. range & \({}^{\circ}\)C & 10–40 \\ I/O connectors & — & USB, Ethernet \\ Safety I/O connectors & — & — \\ Wi-Fi & — & Dual-band; ac/g/n/b \\ Bluetooth & — & 4.0 Low Energy \\ SICK microScan3 & — & 2x S300 (360\({}^{\circ}\)) \\
3D Camera & — & 2x Intel RealSenseTM \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of the MiR100 mobile robot [20].  
Figure 1: Mobile robot MiR100.
# Simulation Study on an ICT-Based Maritime Management and Safety Framework for Movable Bridges
## 5 Methodology
### Bridge Reformation  
Bridge reformation is the process of retracting the bridge to previous fixed conditions to let the bridge vehicle pass through it. The bridge reformation process depends on ensuring zero ships between two Alert Belts, and it is confirmed through another counting algorithm. As mentioned in the section, belt division, two routes are maintained for a ship passing through the movable bridge, and the direction of the ship passing through these routes is transverse. For each route, the buoy at the entry Alert Belt counts the number of ships entered and aggregated it. The buoy at the exit Emergency Belt detects the outgoing ships and counts the number of ships. The subtracted value of exit buoy modifies count from entered buoy; therefore, the aggregated count defines the number of ships remaining in each route. When the number of the remaining ships is zero at both routes based on the final value determined by the buoys, then the bridge proceeds to reform and close again for the passage of bridge vehicles.  
Considering continuous ship transportation through the movable bridge navigation system.  
Number of ships detected by buoy, \(Baa\) at entry Alert Belt of route AB = \(w\)Number of ships detected by buoy, \(Bbe\) at exit Emergency Belt of route AB = \(x\)Number of ships detected by buoy, \(Bca\) at entry Alert Belt of route CD = \(y\)Number of ships detected by buoy, \(Bde\) at exit Emergency Belt of route CD = \(z\)Number of ships remaining at route AB = \(w-x\)Number of ships remaining at route CD = \(y-z\)Therefore, the total number of ships remaining in the bridge navigation system = number of ships remaining at route AB + Number of ships remaining at route CD = \((w-x)+(y-z)\).  
The bridge control system proceeds the reformation process when it confirms that the total number of ships remaining in both routes AB and CD = \((w-x)+(y-z)=0\). The bridge reformation process after the ship crossing is shown in Figure 13.  
In Figure 13, the ship has already crossed the bridge, and the bridge initiates the reformation process. It starts moving back to turn back to the fixed bridge condition for bridge vehicle transportation. The graph shows the distance between ship and bridge starts increasing with time.
# Polynomial Modeling of Noise Figure in Erbium-Doped Fiber Amplifiers
## 3 Materials and Methods
### Noise Figure Dataset  
Once all necessary parameters are obtained, a dataset is created for each device. Additionally, two distinct datasets are generated for the low and high-gain ranges. This separation is crucial because the EDFAs examined in this study are designed to optimize the noise figure independently within each range, leading to different behaviors. Each dataset contains the evaluated noise figure of the device for every unique combination of total input power, target gain and tilt, and channel frequency. These datasets are intended for use in polynomial regression analysis. Each dataset consists of tens of thousands of entries, with a total storage size of a few megabytes each. To ensure both efficiency and readability, each dataset is saved in two formats: one optimized for efficient storage and I/O operations, and another in a human-readable format.
###### Abstract  
This paper experimentally validates the basic feasibility of our proposed blind adaptive array (BAA) with subcarrier transmission power assignment (STPA) scheme using a prototype. The proposed STPA-BAA enables spectrum superposing without any channel state information between two different wireless communication systems: employing STPA-BAA to the secondary system, inter-system interference from/to the primary system can be suppressed. Exploiting the characteristics of constant modulus algorithm (CMA) and power inversion (PI), the secondary transmitter provides two levels of power density for each subcarrier: high and low levels. It enables the secondary receiver to suppress interference with almost the same level of the desired signal. It is also effective in reducing interference to the primary receiver that has no interference suppression function. This paper conducts laboratory experiments of spectrum superposition in two multicarrier systems by wired setup. Though the practical performance of STPA-BAA is limited due to the quantization level or dynamic range of the transceiver, effectiveness of the proposed scheme is confirmed.  
adaptive array; blind algorithm; subcarrier transmit power assignment; spectrum superposing; field programmable gate array Article
## 2 Materials and Methods
### Binary Tree Cover  
The probability surface was used as the primary dataset in classifying tree cover pixels. The surface was split into 305 tiles across NSW where each tile was approximately 55 km by 57 km. These tiles approximate the size and location of the SPOT5 images, although due to the variable look angle of the sensor the images did not conform to the standard paths and rows of nadir-viewing sensors. A probability threshold was manually selected for each tile in order to classify tree cover pixels, as there was insufficient training data for optimum thresholds to be calculated using accuracy statistics. This was done interactively by visually discerning tree foliage in 2.5 m pan-sharpened SPOT5 near-natural imagery acquired during the 2011 summer. Due to variations in vegetation type and structure the effectiveness of any specific threshold varied spatially within each tile. To account for this, most tiles were iteratively refined by identifying sub-tile thresholds for specially digitised areas. This process removed most errors, although commission errors remained in some areas due to the presence of water or the inability of the time series to differentiate green herbaceous vegetation from tree cover. Omission errors from clouds also remained. Each tile was therefore further edited on-screenby visually comparing with the SPOT5 2.5 m imagery. Visible commission errors were manually erased and missing patches of contiguous forest were digitised.  
The probability thresholds varied from 0-40 in the southwest to 60-98 in the northeast and along the coast. Southwest NSW had good separation of trees and non-trees in the probability image, and could have lower thresholds without causing large commission errors. Northeast NSW and coastal areas required higher thresholds to separate areas of cropping and wetland vegetation that had higher probability values due to persistent greenness. There was also severe flooding in this part of the state which affected vegetation growth greatly.
## 3 Temperature-Based ET Models: Advantages, Drawbacks, and Evolution of Research
### Single-Source Surface Energy Balance Models  
Single-source surface energy balance models are based on the principle of focusing on a predominant surface type when estimating \(H\). In this context, the term \"single source\" implies assuming relative homogeneity in surface properties, enabling simplified calculations. Despite the complexity of the actual environment with its diverse land cover types, employing a single-source approach offers a practical and manageable method for estimating ET. This Section provides a list of the well-known single-source surface energy balance models and the advancements and updates made over time.
## 4 Configuration of BESS
### Optimization Steps  
In this paper, the BESS configuration was a single objective optimization issue. The objective function and constraint conditions were also linear, so using Linear Programming to solve this problem was suitable. During the optimization process, the algorithm steps were as follows:  
Step 1: Setting 24 h as the simulation cycle time, the sampling period, \(T_{n}\), based on the optimized time interval, which can decide the number of sample, \(n\), taking the energy of BESS at each sample time as the variable, \(Q(1),Q(2),\ldots,Q(n-1),Q(n)\);  
Step 2: Calculating the power of the BESS of each sample spot based on the sample cycle as \([Q(2)-Q(1)]/T_{n},[Q(3)-Q(2)]/T_{n},\ldots,[Q(n)-Q(n-1)]/T_{n},[Q(1)-Q(n)]/T_{n}\), the positive value means charge, and the negative value means discharge;  
Step 3: According to the maximum DOD, calculating the energy and power of BESS, as shown in Equations (24) and (25);  
\[E=\max(Q(1),Q(2),\ldots,Q(n-1),Q(n))/DOD \tag{24}\]  
\[P=\max\{|[Q(2)-Q(1)]/T_{n}|,|[Q(3)-Q(2)]/T_{n}|,\ldots,|[Q(n)-Q(n-1)]/T_{n}|,| [Q(1)-Q(n)]/T_{n}|\} \tag{25}\]  
Step 4: According to the charging load of the station, using Linear Programing to solve Equation (17) with Constrains (19)-(23).
## 3 Result and Discussion
The frequencies assumed in this work are low frequencies obtained from mechanical system vibration. The efficiency of mapping mechanical energy to electrical energy is more dependent on frequency than amplitude. According to the empirical data as well as the mathematical model, when the frequency of the excitation load is increased, the voltage that is obtained also increases in a proportional fashion. To obtain a clearer picture of the results of the measurement, it is more practical to convert the responses into the frequency domain using the FFT (fast furrier transformation) algorithm. Figure 5 shows the frequency domain obtained by the FFT algorithm, which was used to sample data from the time domain into the frequency domain. According to Figure 6, the voltage response of the laminated MFC piezoelectric transducer increases linearly as the temperature and frequency are increased. The graph demonstrates peak voltage measured 7.2 volts at its highest point at a frequency of 25 Hertz and a temperature of 60 degrees Celsius. The simple supported harvester model and experimental results confirm that the two key parameters of the external loads (frequency and temperature) had the greatest influence on power efficiency (Figure 7). As shown in the frequency domain on Figure 6, as the temperature rises from 25 to 60 degrees Celsius, the voltage drop in the resistor increases smoothly. The same is true for the frequency of excitation; as the frequency increased, so did the amount of energy obtained. As the temperature rises, the thermal coefficients of the laminated MFC expands, influencing the strain induced. Although increasing temperature has an effect on dielectric coefficients [28] the magnitude is small in comparison to thermal coefficients. The thermal expansion of the tested sample is more strongly influenced by an increase in temperature than the dielectric coefficient properties. Due to the dielectric property, it has been observed in a different paper [28] that an increase in temperature results in a decrease in the efficiency of MFC piezoelectric transducers. On the other hand, in this research, we attempted to solve the problem by extending the model to include thermal expansion as a function of strain, as demonstrated by Equation (20). Because of the  
Figure 5: Frequency domain voltage measurement at various temperatures. stress and the thermal expansion, the strain that is caused by this equation will be induced. According to the findings of this study, the thermal expansion of a material increases at a rate that is significantly higher than the rate at which its dielectric coefficient increases when the temperature increases. This causes the voltage to rise at the output of the transducer, which adds new findings to those obtained from the work carried out previously. Figure 7 illustrates the relationship that exists between power, temperature, and frequency in the context of the results that were obtained. Figure 6: Voltage measurement in temperature domain at various frequencies. Figure 7: RMS (root mean square) power obtained in the function of temperature and frequency.
## 4 Enhancement of Energy Recovery (Biogas/Methane Yield) from the Anaerobic Digestion of Rice Straw
### Addition of Trace Elements (TEs)  
Several specific TEs such as iron (Fe), cobalt (Co), nickel (Ni), copper (Cu), zinc (Zn), molybdenum (Mo), selenium (Se), and tungsten (W) are essential for enzyme cofactors involved in the biochemistry of CH\({}_{4}\) formation [104] and needed in a balanced AD [105]. However, the availability of TEs in higher amounts can cause inhibition to anaerobic organisms due to the disruption of an enzyme's structure and function [106]. Therefore, an appropriate amount of TEs is required to avoid the inhibition by maintaining effective growth and metabolism of microorganisms. Moreover, it has been found from the literature that the degree of inhibition depends upon several factors such as the total concentration, pH, redox potential, and chemical forms of the elements [107; 108].  
Recently, [PERSON] et al. [29] added different TEs (Co, Ni, and Se) in various mass fractions to study the enhancement of CH\({}_{4}\) yield in the AD of RS. The results showed a 12% improvement in the CH\({}_{4}\) yield with the addition of these TEs. In another similar study, [PERSON] et al. [109] observed 18% more CH\({}_{4}\) yield when Fe was added to the batch AD of RS. A study conducted by [PERSON] et al. [110] using RS as a sole substrate showed different results as compared to these similar studies [29; 109]. The authors did not observe any enhancement in CH\({}_{4}\) yield due to the addition of Fe and Co. They concluded that it is likely due to the presence of the elevated TEs in the inoculum used in the study. The effect of the addition of various TEs on the methane yield in the AD of straws is summarized in Table 9.  
The results of the abovementioned studies show that research on several TEs has been carried out for single and multiple elements, while Fe, Ni, and Co are the most investigated. The addition of TEs has been successfully used to enhance biogas and CH\({}_{4}\) yield in the AD of various straw types. Specifically, the enhancement in CH\({}_{4}\) yield of 0-18% has been observed due to the addition of TEs (Fe, Ni, Co, and Se) to the AD of RS. Similarly, up to 62% enhancement in CH\({}_{4}\) yield observed with the addition of TEs to other straw (CS) (Table 9).  
Literature data on the impact of TEs addition on the enhancement of CH\({}_{4}\) yield in the AD of RS is coarse. There were only three studies found investigating the effect of the addition of Fe, Ni, Co, and Se. Two studies show that the addition of TEs improves the CH\({}_{4}\) yield while the third study shows there was no improvement. Therefore, there is a need for future work in this area to understand the effect of the addition of other TEs as well. Likely, the addition of suitable elements in an appropriate combination and amount would enhance the CH\({}_{4}\) yield in the AD of RS as evidenced by other straws.
# LESS: Link Estimation with Sparse Sampling in Intertidal WSNs
## 7 Performance Evaluation
### Evaluation in Real Systems
#### 7.2.4 Topology Reconstruction  
We also evaluate the performance of LESS on topology reconstruction using K-Temporal sampling. We test eight traces from different time periods at each sampling rate with K-Temporal sampling in LESS, and the topology recovery accuracy and false positive/negative value are shown in Figure 12c. With the increasing of the sampling rate, the performance of reconstruction accuracy is improved, and over 90% of links are correctly estimated with a sampling rate of 0.4. We look into the false positive/negative statistics and find that both FP and FN are less than 5% when sampling rate is over 0.4. When the sampling rate is extremely low, e.g., 0.1, false positive is over 20% because LESS mistakenly identifies a non-existent link as an existing one. It is also worth mentioning that, even when the sampling rate is 0.2, we can still have a high accuracy, i.e., over 80%, as topology reconstruction requires far fewer data than link quality reconstruction does.
# Few-Shot Knowledge Graph Completion Model Based on Relation Learning
## 2 Related Work
### Semantic Matching-Based Methods  
Semantic matching-based methods use similarity-based scoring functions to mine the potential semantics between entities and relations, aiming to solve the knowledge graph completion task. Among them, the RESCAL model [25] represents entities as vectors and relations as matrices, using a bilinear function as the scoring function and obtaining prediction scores through tensor decomposition. However, as the embedding dimension increases, RESCAL faces the issues of parameter explosion and increased model complexity. To address these problems, the DistMult model [12] constrains the relation matrix to be diagonal, thereby simplifying the model, but this also results in an inability to handle asymmetric relations. The ComplEx model [13] uses complex vectors to represent entities and relations, which can effectively model various binary relations such as symmetric and asymmetric relations. Compared to translation-based methods, semantic matching-based methods have higher complexity, lower model-training efficiency, and weaker generalization ability, but they can better capture the implicit semantics between entities.