esa-sceva/satcom-chunk-collection huggingface dataset:
isolated from _Trichoderma_ species and classified into sesquiterpenes, diterpenes, monoterpenes, and meroterpenoid [9]. Some of these compounds have demonstrated medicinal or agronomic importance [10]. For example, harzianum A (HA), a monoterpene, has demonstrated significant cytotoxicity against human cancer cells [11], and the sesquiterpene 3 isolated from _Trichoderma reesei_ PSU-SPSF013 exhibits antifungal activity against _C. neoformans_ ATCC09112 [12]. As species of _Trichoderma_ with biocontrol potential, terpen produced by _Trichoderma atroviride_ can exert influence on plant growth and development [13; 14]. Up until now, the identified terpenic frameworks derived from _T. atroviride_ include harziane [15; 16], proharziane [16], wickerol [17], cyclonerane [15; 18], and synderane [19]. However, the biosynthetic pathways of these compounds have received relatively limited attention in the _Trichoderma_ spp.  
Terpene synthase (TPS) plays a pivotal role as a regulatory enzyme in the biosynthetic pathway of terpenoids, particularly in governing the remarkable diversity of terpene compounds [20; 21]. In organisms, terpenoids can be synthesized through two distinct pathways, the mevalonate (MVA) pathway and the 2-C-Methyl-D-erythritol-4-phosphate (MEP) pathway [22; 23]. The MEP pathway has been demonstrated to be present in prokaryotes, chlorella, and higher plants [24], while the MVA pathway exists mainly in eukaryotes [23]. In general, terpenoids are synthesized from two C\({}_{5}\) isoprene units, dimethylallyl pyrophosphate (DMAPP) and isopentenyl pyrophosphate (IPP), both of which are catalyzed by terpene synthases to produce a wide variety of open-chain or cyclic compounds through substitution, coupling, cyclization, or rearrangement [25; 26].  
In this study, through the genome sequencing of HB20111 and the subsequent prediction of potential terpene synthases within the genome, a previously unidentified terpene synthase gene, _tps1_, was discovered and subjected to a bioinformatic analysis. To elucidate the function of this gene and identify the terpen it produces, we manipulated the transcription level and expression of _tps1_ by constructing a gene-deletion strain (\(\Delta\)_tps1_) and an overexpressing-engineered strain (O_tps1_). Furthermore, headspace solid-phase microextraction (HS-SPME-GC-MS) was employed to identify and analyze the characteristics of terpene metabolites in _Trichoderma atroviride_ HB20111 and its mutant strains. This enabled us to investigate the role of this gene in the terpene metabolic pathway and provide theoretical guidance for future research on terpene compound synthesis mechanisms in _Trichoderma_.
## 2 System Model
### High-Temperature Loop  
The \(T\)-\(s\) diagram of an HTL is depicted in Figure 2. Two cases for a pinch point temperature difference (PPTD) position of the HTL evaporator are observed. When the PPTD of the HTL evaporator is at an HTL working fluid evaporation bubble point (State point 3, VPP) [8], the HTL mass flow rate is calculated as follows:  
\[m_{\rm H}=\frac{m_{\rm g}c_{\rm pg}\big{(}T_{g1}-T_{g2}\big{)}}{(h_{5}-h_{3})} \tag{1}\]  
\[h_{5}=f(p_{\rm He},T_{5}) \tag{2}\]  
where \(h\) is the specific enthalpy; \(m_{\rm g}\) is the mass flow rate of the engine exhaust gas; \(c_{\rm pg}\) is the specific heat capacity of the engine exhaust gas at constant pressure, which is set to 1.1 kJ/(kg\(\cdot\)K) [42]; \(T_{\rm g}\) is the engine exhaust gas temperature; and \(p_{\rm He}\) is the HTL evaporation pressure.  
Figure 1: Schematic of the studied dual-loop organic Rankine cycle (DORC) system.  
Figure 2: High-temperature loop (HTL) \(T\)-\(s\) diagram.  
When the PPTD is at the working fluid inlet (State point 2, PPP), the HTL mass flow rate is calculated as follows:  
\[m_{\rm H}=\frac{m_{\rm g}c_{\rm pg}\big{(}T_{g1}-T_{g3}\big{)}}{(h_{5}-h_{2})} \tag{3}\]  
\[T_{g3}=T_{2}+\Delta T_{\rm PP1} \tag{4}\]  
The power produced by the HTL turbine can be expressed as follows:  
\[P_{\rm H}=m_{\rm H}(h_{5}-h_{68})\eta_{\rm t}=m_{\rm H}(h_{5}-h_{6}) \tag{5}\]  
where \(\eta_{\rm t}\) is the turbine isentropic efficiency.  
The power consumed by the HTL working fluid pump can be calculated as follows:  
\[P_{\rm Hp}=\frac{m_{\rm H}(h_{28}-h_{1})}{\eta_{\rm P}}=m_{\rm H}(h_{2}-h_{1}) \tag{6}\]  
where \(\eta_{\rm P}\) is the working fluid pump efficiency.  
The HTL net power output is equal to the power produced by the HTL turbine minus the power consumed by the HTL working fluid pump.  
\[P_{\rm Hnet}=P_{\rm H}-P_{\rm Hp} \tag{7}\]
# Predicted Future Changes in the Mean Seasonal Carbon Cycle Due to Climate Change
## 2 Materials and Methods
### Case Study Areas  
Five case studies were selected as representative of the main European forest species (and climate) and at the same time because of long-monitored sites and part of the Fluxnet network [56], the ISMIP (Inter-Sectoral Impact Model Intercomparison Project, [57]; [[https://www.isimip.org/](https://www.isimip.org/)]([https://www.isimip.org/](https://www.isimip.org/)), accessed on 1 January 2020) initiative and the PROFOUND database [42; 58; 59]: the temperate European beech (_Fagus sylatica_ L.) forest of Collelongo, Italy (IT-Col), and of Sore, Denmark (DK-Sor), the maritime pine (_Pinus pinaster_ Ait.) forest of Le Bray, France (FR-Lbr), the boreal Scots pine (_Pinus sylvestris_ L.) forest of Hyytiala, Finland (FI-Hyy), and the temperate Norway spruce (_Picea abies_ (L.[PERSON] [PERSON]) forest of Bily Krz, Czech Republic (CZ-Bk1) (Figure 1). Stand characteristics are described in Table 1.  
Figure 1: Forest classification for broadleaved and coniferous forests at the European level in the spatial resolution of 100 m [60]. Location of the Fluxnet sites considered.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c} \hline \multirow{2}{*}{**Fluanet Code**} & \multirow{2}{*}{**Nation**} & \multirow{2}{*}{**Size**} & \multicolumn{3}{c}{**Coordinate**} & \multirow{2}{*}{**Climate**} & \multirow{2}{*}{**Spices**} & \multicolumn{2}{c}{**Period of Simulation**} & \multirow{2}{*}{**Mean DBH**} & \multirow{2}{*}{**Tree Height**} & \multirow{2}{*}{**Mean Seg**} & \multirow{2}{*}{**Stand**} \\  & & & **Name** & & & & & & & & & & & & \\ \hline IT-Col & Italy & Celliegeop & 41.8 & 13.6 & 1560 & Temperature & _Fegas objects_ & 1997-2014 & 1997-2099 & 20.2 & 17.3 & 105 & 900 \\ RF-Blr & France & Le Bray & 44.7 & -0.8 & 61 & Temperature & _Fegas objects_ & 1998-2008 & 1997-2099 & 26.7 & 17.8 & 26 & 614 \\ H-Hy & Finland & Hy Hytyalki & 61.9 & 24.3 & 181 & Broad & _Fegas objects_ & 1998-2014 & 1997-2099 & 13 & 11.3 & 35 & 870 \\ CZ-Bk1 & Republic & Spohke & 49.3 & 18.3 & 875 & Temperature & _Fegas objects_ & 2000-2008 & 1997-2099 & 9.1 & 7.5 & 19 & 2388 \\ DK-Sor & Denmark & Servo & 55.3 & 11.4 & 40 & Broad & _Fegas objects_ & 1998-2008 & 1997-2099 & 24.3 & 21 & 75 & 353 \\ \hline \end{tabular}
\end{table}
Table 1: Site description and stand initialization data are used in simulations with data from the PROPOUND database [58]. Model initialization data (e.g., diameter, at breast height—DBH, tree height, stand age, and stand density) correspond to the stand conditions of the first year in the historical simulations.
## 3 Hybrid Network Implementation Using LoRaWAN and LoRaMESH
### Implementation of the Hybrid Network Using LoRaWAN and LoRaMESH  
This work studies and proposes the implementation of a strictly LoRaWAN or LoRaMESH and hybrid network with the two protocols for an AMI in the HyDSMaaS scenario using LoRa technology. For that, the implementation was divided in four steps, being (1) LoRaWAN and LoRaMESH protocols implementation, (2) Benchtop protocols performance analysis (testbed), (3) Data Aggregator Point (DAP) device hybridization implementation and finally, (4) real scenario test. The implementation of the protocols takes place through the use of LoRa technologies mentioned above. It is worth noting that each protocol consists of a distinct architecture and devices even with the same technology, the LoRa.
## 1 Introduction  
In industrial plasma processing, such as microwave plasma chemical vapor deposition (PECVD) [1; 2; 3], it would be useful to directly control plasma properties such as electron temperature and density [4]. For plasma diagnostics, the Langmuir is widely used [5; 6]. A Langmuir probe means a probe placed in charged plasma. Though measuring the current-voltage (I-V) curve, the plasma's electron temperature and density can be estimated. A lot of Langmuir probe structures have been researched by a lot of institutions around the world to monitor various plasmas.  
However, when the Langmuir probe is used to measure plasma for a long time, especially in PECVD, as the plasma reaction proceeds, the Langmuir probe will adsorb impurities and become dirty. Moreover, if the electron temperature and density are obtained by calculating this dirty Langmuir probe (I-V) curve, the electron temperature and density would be incorrect [4; 7; 8; 9]. The contamination on the probe surface is very common in various probe designs. The contamination has become a significant problem that limits the application of the Langmuir probe.  
The optical emission spectrometry (OES) is a non-intrusive plasma diagnostics method, which is easy to performed, and it is also frequently used in studies of plasma [10; 11; 12]. Therefore, the plasma reaction cannot bring contamination to the OES. During the plasma reaction, OES can stably measure reliable electron temperature. The Langmuir can measure both the electron temperature and electron density. Both electron temperature and electron density are very important for diagnosing plasma. However, as the plasma reaction progresses, the contamination is attached to the probe surface. Most of these clean probe methods need to interrupt the plasma reaction. However, in the experiment, the result shows that the contamination on the Langmuir probe has little effect on the ion saturation current, as reported in other articles [7; 8; 13]. In other words, the ion saturation does not change. Therefore, if the electron temperature _Tev_ can be measured by optical emission spectrometry (OES), the electron density can be calculated from the I-V curve of the probe with contamination. Therefore, combining the Langmuir probe and OES, both electron temperature and density would be measured, even if the probe has been contaminated. In other words, the cleaning interval of the Langmuir probe would be significantly extended.  
Based on the above proposal, a verification experiment is carried on and presented in this paper. The experiment has been done in a homemade coaxial line microwave plasma source driven by 2.45 GHz magnetron and using argon as feeding gas. The results show that the contaminative Langmuir probe I-V curve with the OES can give the approximately electron temperature and density. Besides, by measuring the electron temperature and density under different pressures (40-80 Pa) and microwave power (400-800 W), it is proved that this method is feasible.
# Guidelines towards Information-Driven Mobility Management
## 6 Conclusions and Future Research Directions  
The benefits provided by the intrinsic ICN architectural design in regards to mobility are the basis to rethink mobility management widely and from a content-centric perspective. The ICN architectural design removes the need for a functional centralised or de-centralised mobility anchor-point. As such, anchor-less approaches seem to be a relevant approach as they reduce the need for additional state, and allow ICN supporting mobility management in a data-centric way.  
Consumer mobility is well supported from a network architectural perspective, but there is the need to understand performance impact derived from the type of movement as well as from the types of topologies. The pull-based nature of ICN gives the means to prevent serious packet loss; nevertheless, consumer mobility may still result in large time-to-completion intervals.  
In what concerns producer mobility, the support is not intrinsic, and the receiver-driven, pull-based ICN approach requires adjustments to fully support producer mobility. Multi-homing is well supported.  
While ICN has relevant architectural properties which seem to provide better and integrated support for mobility management, there are a few aspects requiring further research. A first future research direction concerns a better support for mobility management via NDN routing. By devising routing approaches that are sensitive to node movement [30], Interest packets can be forwarded in a way that is automatically based on individual and collective roaming habits of devices, eventually reducing the need to perform re-registration once nodes reattach. This can be done by developing forwarding strategies based on mobility prediction, or by integrating routing support based on context-awareness [31]. A second research direction is to perform an analysis of the ICN mobility support support in highly variable topologies, where anchor-less strategies may not suffice to adequately support producer mobility. For this case, both producer and consumer need to be considered as mobile entities and, therefore, any future mobility management approach should simply look into the support of a single mobile entity, instead of supporting, as previously, a MN and a CN entity separation.  
Work developed in this paper was funded by FCT strategic project COPELABS, references UID/MULTI/04111/2013, UID/MULTI/04111/2016, and UID/MULTI/04111/2019.  
**Acknowledgments:** [PERSON] thanks the insightful exchange of ideas concerning ICN and IoT held in 2017/2018 with [PERSON] (Siemens AG), [PERSON] (Siemens AG), [PERSON] (Siemens AG and Technical University of Munich), and [PERSON] (Technical University of Munich).  
**Conflicts of Interest:** The author declares no conflict of interest.
# Deep Reinforcement Learning for UAV-Based SDWSN Data Collection
## 3 System Model
The main parameters for designing an optimal UAV path within the fuzzy region considering the RL algorithm are presented in Figure 1. As highlighted in this figure, the parameters are organized into two main categories: parameters that define the UAV energy consumption model and parameters that define the UAV-Gateways communication throughput. Based on the given UAV energy consumption Equations (2), the UAV energy propulsion usage is highly dependent on the instantaneous acceleration, velocity, and position factors. Since UAV acceleration and velocity are proportional to the amount of second and first derivatives of the instant position, the UAV energy consumption is dependent on the instant location q(t) ultimately. On the other hand, based on the emerged equation of the UAV communication throughput in (1), it would be the same case with communication throughput. This means  
Figure 1: Relevant parameters for RL algorithm definition. that the UAV communication throughput is highly dependent on the instant location of the UAV. Hence both UAV energy consumption and communication throughput are dependent on the UAV position at each time slot by two various equations. To define the approximation optimization problem, a cost function of multiple parameters is required to be specified as:  
\[ \begin{array}{ccc}\text{Minimise}&&\text{q(t)}&\overline{\mathbb{E}(\text{q(t) })_{\text{UAV}-\text{propulsion}}}\\ &\text{s.t. List of Contraints}&&\end{array}  \tag{3}\]  
where \(\text{q(t)}\) is the UAV path design. \(\overline{\mathbb{E}(\text{q(t)})_{\text{UAV}-\text{propulsion}}}\) is the UAV propulsion power consumption obtained from (2), \(\overline{\mathbb{R}(\text{q(t)})_{\text{air}-\text{to-Ground}}}\) is the communication throughput obtained from (1). According to our assumptions, ground network energy consumption is fixed and not dependent on the location of the UAV, while air-to-ground energy consumption is highly dependent on the distance between the UAV and SNs and as a result, the locations of the UAV. Hence, the impact of the ground SNs' energy consumption while communicating with each other on the ground is disregarded in our optimization problem formulation by only taking the UAV-Ground communication energy usage (Cost\({}_{\text{air}-\text{to-Ground}}\)) into account.
## 4 Mobile Localization Algorithms
### Distributed Localization Algorithms
#### 4.2.1 Multi-Frequency Active Localization Method Base on TDoA (MFALM)  
In mobile UWSNs, sensor nodes' locations are changed at any time. Location information at a time cannot serve as a reference at the next time. Moreover, in general, only the location information of sensor nodes which detect events is useful for UWSNs. Therefore, it is not necessary to localize all sensor nodes in network. We only need to localize sensor nodes which detect events. In [44], the authors propose a Multi-frequency Active Localization Method base on TDoA (MFALM). There are three types of nodes: buoy nodes, relay nodes and ordinary nodes. After the network is deployed, buoy nodes firstly localize themselves using GPS, and periodically broadcast localization information with low-frequency acoustic signals. Relay nodes communicate with each other with low-frequency acoustic signals to divide the network into multiple localization domains and calculate the value of max hops for each domain. Ordinary nodes which detect event open low-frequency signal receiving devices to receive localization information from buoy nodes and localize themselves. At the same time, the ordinary nodes open high-frequency signal sending devices to broadcast MRP (Message Report Package). MRP contains the detected events and the locations of the ordinary nodes. All ordinary nodes which receive the MRP need to relay the packages and decrease the value of max hops by 1. The package broadcasting stops when the value of max hops is 0 or the MRP is received by any relay node. Relay nodes send the received MRP to buoy nodes for further disposal. After disposing the MRP, buoy nodes will respond ACK to ordinary nodes. Then, the ordinary nodes go to sleep until detecting new event.
## 3 Results  
### Accuracy Assessment of STARFM  
Although our study area falls into the area of a previous study [32] which validated STARFM's ability to fuse images in our general location, a brief accuracy assessment was performed to ensure the heterogeneous urban area of our study did not significantly alter the accuracy of synthetic Landsat images. To assess the accuracy, we compared reflectance values of several Landsat surface reflectance images to the synthetic Landsat scene that was closest in date. The comparison was made by reporting both the R\({}^{2}\) values and the mean absolute difference (Table 3). None of these Landsat images that were used in validation were used as a part of a base pair in STARFM. In addition, the scenes chosen represent both leaf-on conditions and leaf off conditions.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Informal Selection** & **Day 21, Year 2003** & **Day 181, Year 2003** & **Day 245, Year 2003** & **Day 58, Year 2005** & **Day 298, Year 2007** & **Day 128, Year 2007** & **Day 2008, Year 2007** \\ \hline
**Mean Abs. Diff. All Areas** & 0.07 & 0.04 & 0.05 & 0.07 & 0.07 & 0.12 & 0.06 & 0.04 \\ \hline
**Mean Abs. Diff.** & & & & & & & & \\
**Urban Locations** & 0.07 & 0.04 & 0.04 & 0.06 & 0.06 & 0.11 & 0.05 & 0.04 \\ \hline
**Mean Abs. Diff.** & & & & & & & & \\
**Exurban Locations** & 0.09 & 0.06 & 0.07 & 0.08 & 0.08 & 0.14 & 0.07 & 0.04 \\ \hline
**R\({}^{2}\) All areas** & 0.59 & 0.85 & 0.87 & 0.72 & 0.72 & 0.36 & 0.81 & 0.89 \\ \hline
**R\({}^{2}\) Urban Locations** & 0.66 & 0.91 & 0.92 & 0.77 & 0.8 & 0.41 & 0.84 & 0.91 \\ \hline
**R\({}^{2}\) Exurban Locations** & 0.45 & 0.69 & 0.8 & 0.63 & 0.54 & 0.28 & 0.71 & 0.85 \\ \hline
**Statistical Selection Method** & \begin{tabular}{c} **Day 21, 2003:** \\ **95\% Selection** \\ \end{tabular} & \begin{tabular}{c} **Day 250, 2005:** \\ **95\% Selection** \\ \end{tabular} & \begin{tabular}{c} **Day 250, 2005:** \\ **95\% Selection** \\ \end{tabular} & \begin{tabular}{c} **Day 250, 2005:** \\ **90\% Selection** \\ \end{tabular} & \begin{tabular}{c} **Day 250, 2005:** \\ **95\% Selection** \\ \end{tabular} & \
\begin{tabular}{c} **Day 43, 2011:** \\ **95\% Selection** \\ \end{tabular} &
\begin{tabular}{c} **Day 43, 2011:** \\ **95\% Selection** \\ \end{tabular} \\ \hline
**Mean Abs. Diff All Areas** & 0.05 & 0.04 & 0.04 & 0.11 & 0.17 \\ \hline
**Mean Abs. Diff Urban Locations** & 0.05 & 0.04 & 0.04 & 0.11 & 0.22 \\ \hline
**Mean Abs. Diff Eurban Locations** & 0.05 & 0.05 & 0.06 & 0.11 & 0.15 \\ \hline
**R\({}^{2}\) All areas** & 0.68 & 0.88 & 0.87 & 0.46 & 0.16 \\ \hline
**R\({}^{2}\) Urban Locations** & 0.72 & 0.9 & 0.92 & 0.54 & 0.19 \\ \hline
**R\({}^{2}\) Eurban Locations** & 0.57 & 0.82 & 0.76 & 0.35 & 0.13 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Accuracy assessment results showing R\({}^{2}\) values and the mean absolute difference between synthetic NDVI values and Landsat surface reflectance NDVI values that were not used in data fusion.
# A Framework for Fall Detection Based on OpenPose Skeleton and LSTM/GRU Models
## 2 System Architecture and Method
### Model Architecture
#### 2.4.1 Recurrent Neural Network (RNN)  
RNN [29; 30] can describe dynamic behavior, as shown in Figure 10. Unlike the feed-forward neural network (FNN) that accepts more specific structure input, RNN transmits the state in its own network cyclically, so it can accept a wider range of time series structure input.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Original Joint Points** & **Missing Joint Points** & **Percentage** \\ \hline No-Normalization & 2,850,000 & 520,112 & 18.2\% \\ RP-Normalization & 1,710,000 & 214,707 & 12.5\% \\ Linear Interpolation + & 1,710,000 & 27,849 & 1.6\% \\ RP-Normalization & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Proportion of missing points.  
Figure 10: Recurrent Neural Network (RNN) [29].  
Figure 9: Linear interpolation compensates for missing points (points 10–14, points 16–20 are missing).  
The difference between RNN and FNN is that RNN can pass the calculated output of the \(h_{t-1}\) layer back to the \(h_{t}\) layer as input, and \(o\) is the output vector. The two are defined as [29]  
\[h_{t}=\sigma_{h}(Vh_{t-1}+U_{h}x_{t}+b_{h}) \tag{9}\]  
and  
\[o_{t}=\sigma_{o}(W_{o}h_{t}+b_{o}) \tag{10}\]  
respectively. Here, \(t\) is time, \(h\) is the vector of the hidden layer, \(x\) is the input vector, \(V\), \(W\), and \(U\) are the parameter weights, \(b\) is the partial weight, and \(\sigma_{h}\) and \(\sigma_{o}\) are both excitation functions.
# To What Extent Are the Effects of UV Radiation on Grapes Conserved in the Resulting Wines?
## 3 Discussion  
The present study is one of few evaluating how the UV radiation received by grapes determines differences in the resulting wines, taking into consideration diverse variables, such as phenolic compounds, VOCs, antioxidant capacity, and sensorial characteristics (color). In addition, this is the first study to our knowledge differentiating between the effects of UV-A and UV-B wavelengths from grapes to wine.