esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Stuck Fermentation Altered the Profile of Non-Volatile Metabolites in Summer Black Tea
#### 3.2.1 Amino Acids and Derivatives  
The scent and flavor of the tea soup are benefited by the presence of free amino acids [19]. We identified 56 differential amino acids and derivatives between the YH and FHYH samples, of which 6 were significantly upregulated in FHYH, while the remaining 50 showed a decreasing trend (Figure 3A). These findings are consistent with the study of [PERSON], who reported a general decrease in the free amino acid content of instant tea powder after liquid-state fermentation [17]. Therefore, the decrease in amino acid concentration may be applied to clarify why FHYH is less fresh. Specifically, we detected a considerable decrease in L-glutamine (CAS:56-85-9), L-tyrosine methyl ester (CAS:1080-06-4), L-proline (CAS:147-85-3), L-isoleucine (CAS:73-32-5), L-tyrosine (CAS:60-18-4), L-tryptophan (CAS:73-22-3), L-histidine (CAS:71-00-1), L-leucine (CAS:61-90-5), L-valine (CAS:72-18-4), and L-phenylalanine (CAS:63-91-2). Previous studies have shown that tryptophan, L-tyrosine methyl ester, L-proline, L-isoleucine, and L-tyrosine are the determinants of the astringent and bitter taste of tea, while L-glutamine (CAS:56-85-9) impart a salty taste to tea soup [10;16]. Furthermore, several bitter amino acids (e.g., histidine, leucine, valine, and phenylalanine) can be attenuated by \(\gamma\)-glutamyl [20], which may be responsible for the reduced bitterness and astringency of FHYH. Similar results were published by [PERSON] et al., who demonstrated that following microbial fermentation, tea had less L-glutamic acid, L-norleucine, L-phenylalanine, and L-tyrosine [21]. In addition, the glycoconjugated amino acids L-glutamine-O-glycoside and L-glutamic acid-O-glycoside, which impart a sweet taste to tea soup by releasing glycosides, also decreased after stuck fermentation. We also detected a significant accumulation of N-acetyl amino acids, such as N-acetyl-L-tryptophan (CAS:1218-34-4) and N-acetyl-L-glutamine (CAS:2490-97-3), in FHYH. Likewise, [PERSON] et al. also found that several N-acetyl amino acids increased in tea after microbial fermentation  
Figure 2: Analysis of non-volatile metabolites in YH and FHYH groups. (**A**) The categories of 1248 non-volatile metabolites. (**B**) Principal component analysis (PCA) of YH and FHYH groups. PC1 is the first principal component. PC2 is the second principal component. (**C**) Hierarchical clustering analysis (HCA) of 1248 non-volatile metabolites. (**D**) Orthonormal partial least squares discriminant analysis (OPLS-DA) of YH and FHYH. (**E**) Heat map of differential metabolites. (**F**) KEGG pathway enrichment analysis of significantly different non-volatile metabolites.  
tion, and predicted their potential neuroprotective effects by molecular docking [22]. To summarize, the decrease in the content of amino acids and their derivatives due to stuck fermentation alleviated the bitterness and astringency of tea soup, decreased the freshness, and increased sweetness.
# LoRa Technology Propagation Models for IoT Network Planning in the Amazon Regions
## 3 Measurement Methodology
### Measurement Scenario  
The objective of this work is to characterize the channel for LoRa technology in coastal environments to facilitate the implementation of telecommunication networks serving the riverside communities of the Amazon region.  
The chosen measurement scenario is the edge of UFPA, featuring a predefined route along the banks of the Guama River. The area includes dense and tall vegetation, hindering the line of sight in many parts, along with medium-height buildings and open spaces used as parking lots. The study route spans 1461 m along the campus edge, extending from the Basic sector to the Health sector. Using the Google Earth tool, Figure 5 shows a satellite image of the measurement scenario.  
Observing from the heights of the gateway and the end node, it is evident that the majority of the route is obstructed by vegetation and some buildings, as depicted in Figure 5. The portion obstructed by vegetation constitutes 1133 m, equivalent to 77% of the total route.  
As mentioned in Section 2, uplink communication occurs between the device and the gateway, and the received data are then considered for calculating the RSSI, SNR, and packet efficiency at the gateway. Additionally, GPS data are collected to determine the locations of the measured points.  
Due to the high battery consumption of the drone, we repeated the procedure four times, resulting in a total of 497, 437, and 461 points collected for SF7, SF8, and SF9, respectively. We captured 1395 sets of raw RSSI and SNR data for uplink. The signal exhibited similar behavior to the distance; on average, only four measurements were sufficient.  
Figure 4: Lora Gateway RAK7249.  
Figure 5: The measurement scenario.  
The motivation for choosing this environment stems from the significant importance of rivers in the Amazon region for transportation and the economy. By employing sensing with LoRa technology, we can locate a boat along the shores of UFPA Orla and obtain crucial data from boats that follow a similar route. These features underscore the importance of studying LoRa performance and conducting large-scale channel characterization in environments with extensive vegetation morphology, rivers, and suburban areas.  
We adopted an average drone speed of 25 km/h, repeating the measurements four times along the route. Considering the average speed that a small vessel maintains for hours, it becomes evident that the ideal cruising speed falls within the range of 15 to 26 knots, corresponding to approximately 27.78 km/h to 48.15 km/h [36]. The distance between the gateway and the end node was determined by GPS coordinates, and we adopted an average distance of 50 m between the drone and the UFPA coast along the entire route. The route is shown in Figure 6.
## 3 Cascaded Weighted Sum
### Definition of the Cascaded Weighted Sum  
In the _cascaded weighted sum_ (CWS) each objective is assigned a weight \(w_{i}\) as with the pure weighted sum and a priority starting with 1 as the highest one. If desired, some objectives may have the same priority. All objectives but those with the lowest priority receive a user-given threshold \(\varepsilon_{i}\). In the beginning, only the objectives of the highest priority are _active_ and contribute to the weighted sum. The others are activated according to the following priority rule:  
If all objectives with the same priority exceed their threshold, the objectives of the next lower priority are activated and their values are added to the sum.  
As the objectives are grouped by the priorities and the groups are considered one after the other, the method is called _cascaded_ weighted sum. A group whose members exceed their threshold is called a _satisfied group_. If at least one objective of a satisfied group drops below its threshold, the group is not satisfied anymore and consequently, all groups with lower priorities are deactivated, which will significantly reduce the resulting weighted sum.  
For the formal definition of the CWS given in Equation (7), the original \(f_{i}(x)\) are used for the threshold value checks rather than their normalized counterparts, as we assume that this is more convenient for experts of the application. The \(k\) objectives are sorted according to their priorities and we have \(g\) objective groups, where \(1<g\leq k\). For \(g=1\), the CWS would be identical with the weighted sum. Each group consists of \(m_{j}\) objectives, the sum of which is \(k\). As with the original weighted sum, each \(w_{i}>0\) and \(\sum\limits_{i=1}^{k}w_{i}=1\).  
As there are differences for the first and the last priority group, Equation (7) shows the objectives contributing to the weighted sum for the highest priority 1, the general case of priority \(j\), and the lowest priority \(g\).  
**Priority 1**: \(\quad\quad\) if \(\textsf{not all}\;\;f_{i}(x)\geq\varepsilon_{i}\quad\forall i=1,\ldots,m_{1}\) (highest priority) \(\quad\quad\quad\) maximize \(\sum\limits_{i=1}^{m}w_{i}f_{i}^{nom}\big{(}x\big{)}\), \(x\in S\), \(m=m_{1}\)  
**Priority \(j\): \(\quad\quad\) if \(\textsf{all}\;\;f_{i}(x)\geq\varepsilon_{i}\quad\forall i=1,\ldots,l_{j}\; \;\textsf{and}\quad\;\;l_{j}=\sum\limits_{i=1}^{i-1}m_{i}\;\;\;\textsf{(satisfied groups)}\)**  
\(\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad \quad\quad  
The selection of appropriate weights and threshold values requires some knowledge about the problem at hand, including one or more preparative Pareto optimization runs as illustrated in the next section. Thus, neither the original weighted sum nor the CWS are a priori methods. We will come back to this later.
###### Abstract  
Leaf area index (LAI) remote sensing data products with a high resolution (HR) and long time series are in demand in a wide variety of applications. Compared with long time series LAI products with 1 km resolution, LAI products with high spatial resolution are difficult to acquire because of the lack of remote sensing observations in long-term sequences and the lack of estimation methods applicable to highly variable land-cover types. To address these problems, we proposed a recursive update model to estimate 30 m resolution LAI based on the updated Nonlinear Auto-Regressive with Exogenous Inputs (NARX) neural network and MODIS time series. First, we used a variety of HR satellite remote sensing observations to produce HR datasets for recent years. Historical low spatial resolution MODIS products were employed as background information and used to calculate the initial parameters of the NARX neural network for each pixel. Subsequently, one year's reflectance from the HR dataset was used as the new observation that was input into the NARX model to estimate the HR LAI of that year, and the background and HR data were then used for remodeling to update the NARX model parameters. This procedure was recursively repeated year by year until both MODIS background data and all HR data were involved in the modeling. Finally, we obtained an LAI time series with 30 m resolution. In the cropland study area in Hebei Province, China, the results were compared with LAI measurements from ground sites in 2013 and 2014. A high degree of similarity existed between the results for the two study years (RMSE\({}_{2013}=0.288\) and RMSE\({}_{2014}=0.296\)). The HR LAI estimates showed favorable spatiotemporal continuity and were in good agreement with the multisample ground survey LAI measurements. The results indicated that for data with a rapid revisit cycle and high spatial resolution, the recursive update model based on the NARX neural network has excellent LAI estimation performance and fairly strong fault-tolerance capability.  
LAI; time series; high spatial resolution observation; recursive update model; NARX
# Location-Aware Point-to-Point RPL in Indoor IR-UWB Networks
###### Abstract  
Wireless multi-hop ad hoc routing is one of the critical design factors that determine the network performance of various wireless IoT applications. IETF has standardized the point-to-point RPL (P2P-RPL) routing protocol to overcome the inefficient routing overheads of RPL. However, P2P-RPL propagates the route discovery forwarding packets throughout the whole network. P2P-RPL suffers from the high energy consumption and the huge route discovery overhead in low-power and lossy networks (LLNs). In this paper, we propose a novel Location-Aware P2P-RPL (LA P2P-RPL), which achieves the energy-efficient P2P data delivery without reducing the networking reliability. The proposed algorithm introduces the Impulse-Response UWB (IR-UWB) based cooperative multi-hop self localization algorithm and the Location-Aware P2P-RPL algorithm for Indoor IR-UWB based networks. To increase the localization accuracy, smartphone based Inertial Navigation System (INS) with particle filtering is used in indoor multi-hop environments. The performance evaluations for Location-Aware P2P-RPL algorithm are compared with the traditional P2P-RPL and the ER-RPL algorithm to show the significant performance improvements for route discovery overheads and energy consumptions in LLNs.  
P2P-RPL; energy efficient routing; UWB multi-hop localization Article
# Prediction of PM2.5 Concentration on the Basis of Multitemporal Spatial Scale Fusion
###### Abstract  
While machine learning methods have been successful in predicting air pollution, current deep learning models usually focus only on the time-based connection of air quality monitoring stations or the complex link between PM2.5 levels and explanatory factors. Due to the lack of effective integration of spatial correlation, the prediction model shows poor performance in PM2.5 prediction tasks. Predicting air pollution levels accurately over a long period is difficult because of the changing levels of correlation between past pollution levels and the future. In order to address these challenges, the study introduces a Convolutional Long Short-Term Memory (ConvLSTM) network-based neural network model with multiple feature extraction for forecasting PM2.5 levels in air quality prediction. The technique is composed of three components. The model-building process of this article is as follows: Firstly, we create a complex network layout with multiple branches to capture various temporal features at different levels. Secondly, a convolutional module was introduced to enable the model to focus on identifying neighborhood units, extracting feature scales with high spatial correlation, and helping to improve the learning ability of ConvLSTM. Next, the module for spatiotemporal fusion prediction is utilized to make predictions of PM2.5 over time and space, generating fused prediction outcomes that combine characteristics from various scales. Comparative experiments were conducted. Experimental findings indicate that the proposed approach outperforms ConvLSTM in forecasting PM2.5 concentration for the following day, three days, and seven days, resulting in a lower root mean square error (RMSE). This approach excels in modeling spatiotemporal features and is well-suited for predicting PM2.5 levels in specific regions.  
air pollution; PM2.5; convolutional LSTM; spatiotemporal prediction +
Footnote †: journal: Applied  
Article  
Prediction of PM2.5 Concentration on the Basis of Multitemporal Spatial Scale Fusion
## 2 The Application of Metamaterials
### Energy Storage  
Important and growing in popularity application of electromagnetic and mechanical metamaterials is in energy storage. MM can be used to create batteries that are more efficient and have a higher energy density than traditional batteries. This could lead to the development of smaller and more powerful batteries, which would be useful in a wide range of applications, including electric cars and portable electronics.  
[PERSON] et al. [36] demonstrated how to design an energy-storage metamaterial with enhanced mechanical properties and battery safety simultaneously. Mechanical properties of MM could be simultaneously manipulated via their architectures. In the same study, he proposed multifunctional metamaterials, possessing both load-bearing capacity and storage capability, comprising multi-phase lattice MM and cylindrical battery cells. Printedmetamaterials were assembled with battery cells and compresses. Experimental results showed that the void in lattice MM could guide deformation mode away from the internal battery cell that postponed the emergence of battery short-circuit. They can absorb greater energy after defective phase incorporation.  
[PERSON] et al. [37] faced a problem with batteries in electric vehicles. The challenge is to make the design of lightweight and safe battery pack a critical issue. He proposed a multifunctional battery system comprising cylindrical battery cells and surrounding lightweight lattice metamaterial. Lattice density distribution was optimized to minimize stress on the battery during compression where compression testing of the assembled structural battery system revealed that the stronger lattice units in the X-shaped pattern resisted deformation and helped delay of a battery short circuit.
## 3 Results and Discussion
### Flocculation  
Flocculation is a very important ability of brewer's yeast [31], which can be defined as asexual cell aggregation that is reversible and calcium-dependent. The process creates flocs that contain a large number of cells that quickly settle to the bottom of the liquid growth medium [32]. The brewing industry often uses beer yeast flocculation as a simple and cost-effective way to separate yeast cells from fermentation products [33]. The results of the flocculation of the four strains of brewing yeast are presented in Table 2. Flocculation of less than 20% indicates non-flocculating yeast. Flocculation above 85% indicates very flocculent yeast, with values from 20 to 80% being moderately flocculent strains [25]. Among the analyzed yeast, the lager yeast strain _Sp_ W30/70 (poor quality) showed the highest flocculation (70.2%). Which is in line with the data presented in the literature that lager yeast is characterized by a greater flocculation capacity compared to the ale yeast strains [34]. A significantly lower value was observed for _S. cerevisiae_ strains. From the obtained results it can be concluded that the strains _Sp_ W30/70, _Sc_ US-05 and _Sc_ T58 are moderately flocculent strains and the _Sc_ S33 is a non-flocculating strain. When analyzing the effect of different rehydration medium (PTWAir, PTWN, CW) on the quality parameters of yeast, significant changes were noticed in all of the described strains. In the case of the lager strain _Sp_ W30/70, rehydration in the presence of PTWAir and PTWN significantly contributed to the reduction in flocculation of the analyzed yeasts (60.2% and 46.1%, respectively) compared to the control sample (CW). In the case of ale yeast, PTWs contributed to the change in the flocculation capacity of yeast to a different extent. For _Sc_ S33 and _Sc_ T58 yeast strains (poor quality) the PTWN significantly increased the yeast flocculation capacity compared to the control samples (CW) but not enough to consider this strain as very flocculent. Yeast strain _Sc_ US-05 (fresh strain), after rehydration in the presence of PTWN, was characterized by reduced flocculation capacity. PTWAir for all of the _Sc_ yeast strains significantly contributed to the increase in this parameter.
# Output Power Smoothing Control for a Wind Farm Based on the Allocation of Wind Turbines
## 5 Conclusions  
This paper proposed a novel power smoothing control strategy for the wind farm by using the allocation of wind turbines. The WTs of the wind farm are divided into two classes: CWTs and PWTs with different control strategies. The PWTs were controlled to output the maximum power by combining with the rotor inertia based power smoothing method. Power given control was applied to the CWTs. The CWTs and small capacity BESS worked together to take charge of the total power output of the wind farm. The BESS was controlled to participate only when the power limit of CWTs was reached to reduce the charge and discharge times.  
Figure 14: Time histories of the wind farm when the wind speed condition of CWTs is changed. (**a**) wind speed of CWTs. (**b**) Rotor torque of CWTs. (**c**) Output power of CWT (WT7). (**d**) Output power of CWT (WT8). (**e**) Output power of CWT (WT9). (**f**) Total output power of the wind farm.  
The wind farm model was built based on Matlab/Simulink to verify the proposed power smoothing control strategy by considering the wake effect. The grid-connected converter control was also adopted to fulfill the complete functionality of the wind farm. Three working modes of the wind farm as the MPPT control, power smoothing control, power given control are given in the simulation. It has been shown that the output power can meet the different needs of the grid. The effectiveness of the proposed power smoothing strategy was verified by the simulation results.  
[PERSON] was in charge of the work listed as: conceiving and proposing the control strategies, designing the simulations, making charts and writing the paper; [PERSON] analyzed the simulation data and drew some of the charts. [PERSON] and [PERSON] analyzed and confirmed the simulation parameters, diagrams and results.  
This work was supported in part by the National Natural Science foundation of China (51507050), the Natural Science foundation of Jiangsu Province (BK20150822), the Fundamental Research Funds for the Central Universities (Project No. 2018B15314) and Science and Technology Project of SGCC (J2017071).  
The authors declare no conflicts of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
# Optimization Technique for High-Gain CMOS Power Amplifier for 5G Applications
## 2 Design and Optimization of the Power Amplifier
### Size Determination of the Unit Amplifier Transistor  
The RFCMOS transistors used in this study had a gate length of 65 nm with a maximum of 32 fingers. The gate lengths of the transistors used in this study are all 65 nm. The transistor size was adjusted by changing the width of the unit finger, total number of fingers, and multiplier. Figure 1 shows the maximum available gain (MAG) according to the transistor size at 350 mV gate bias voltage. As the gate width changes from 80 \(\upmu\)m to 256 \(\upmu\)m, the single-stage MAG ranges between 8 and 11.5 dB at 28.5 GHz.  
To determine the size of the transistor unit cell and the power stage, simulation of the output power, PAE, and load-pull of the unit amplifier was conducted; moreover, electromagnetic (EM) simulations of the transistor connections in the multiplier were also conducted. As seen in Figure 2, the output power increases with an increase in transistor size. However, PAE decreases, indicating inefficiency in further increasing the transistor size to achieve increased output power. Therefore, it is important to determine the (i) size of the transistor unit cell of the power stage, (ii) target output power, and (iii) the PAE to determine the appropriate transistor size. Particularly, to determine the size of the transistor, we must first determine the finger width, number of fingers, and multiplier size; however, the performance differs in each case due to parasitics, as shown in Figure 2. For example, the smaller the finger width, the smaller is the parasitic gate resistance inside the transistor; therefore, the 4 \(\upmu\)m finger width (red line) has a much lower PAE. For 1 \(\upmu\)m finger width (black line), many multipliers must be used to achieve high power, which results in lower PAE owing to the parasitic component in the power cell layout. Thus, we used unit cell transistors in the power stage with a 2 \(\upmu\)m finger width in this study. Additionally, 32 fingers and 4 multipliers (total gate width: 256 \(\upmu\)m) were used to achieve the target output power of 15 dBm with margin. In this study, we designed a three-stage PA with transistor gate widths of 144, 192, and 256 \(\upmu\)m for each stage with a MAG of 10.5, 9.5, and 8 dB, respectively, and an expected total gain of 28 dB.  
Figure 1: MAG of the unit amplifier based on the transistor gate width.