esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Proposed System
### Action Recognition Module  
Compared to traditional methods that use spatio-temporal features to describe human motion (such as HOGHOF [32], STHOG [33; 34], STGGP [35] and 3 DHOG [36]), recently developed GCNs can recognize human actions more accurately by using the key points extracted from their bodies as the input features. As illustrated in Figure 11, after obtaining the key skeleton points from a real UAV pilot, all of these sequential key points will be transmitted to the spatial-temporal GCN (ST-GCN [19]) module for the action recognition task.  
\[f_{out}^{t}(\mathbf{x}_{c})=\sum_{h=1}^{K}\sum_{w=1}^{K}f_{in}(\mathbf{S}( \mathbf{x}_{c}),h,w)\cdot\mathbf{W}(h,w) \tag{1}\]  
Here, \(\mathbf{S}\) represent the sampling function used to enumerate the neighbors of location \(\mathbf{x}\), and \(\mathbf{W}\) represent the weight function which provides a weight vector for computing the inner product with the sampled input feature vectors within the convolutional kernel. \(h\) and \(w\) represent the pixels within the kernel. In this paper, six kinds of actions (named as \(a_{i},i=1\sim 6\)) are used to control the flight  
Figure 10: The real UAV pilot and his hands will be correctly extracted and the other human and background noise will be ignored.  
Figure 9: Diagram of pilot action detection module.  
action of UAV. The probability of an input action \(a^{(in)}\) to be classified as action \(a_{i}\) is computed from a softmax function \(P(a_{i}|a^{(in)})\) through \(M\) successive frames as:  
\[P(a_{i}|a^{(in)})=softmax(\sum_{t=1}^{M}\sum_{c=1}^{N}f_{out}^{t}(\mathbf{x}_{c} )). \tag{2}\]  
In the case that the maximum output value of this function is over a predefined threshold, the input action will be considered as the corresponding action. A more detailed explanation of the ST-GCN can be found in [19].
## 5 Prospect of Colloidal Silica as a Grouting Material
### Advantages of CS as a Grouting Material
#### 5.2.2 Environmentally Friendly  
The production of cement is an energy-intensive process which consumes massive amounts of coal, discharging many harmful gases in the production process [88]. The chemical grouting materials, such as acrylamide, polyurethane, and epoxy, have better penetrability when compared to cement-based grouting materials, but they are expensive and may cause pollution to underground waterways if handled improperly [89]. The poisoning incident of Fukuoka in 1974, for example, was caused by acrylamide grouting materials for ground improvement. Since then, Japan's Ministry of Construction has set rules that allow only soluble silica to be adopted as a chemical grouting material to improve liquefaction resistance [90]. CS is a kind of nanomaterial and a derivative of soluble silica. It is nontoxic, chemically and biologically inert, and environmentally friendly. When added to soil as a grouting material, CS will not cause pollution, which is known on a basis of learned knowledge. Environmental scientists have also noted that great attention should be paid to the behaviors and application of nanomaterials, with CS included. Thus, CS is a practically potential grouting material due to its environmental-friendly feature.
# DARAL: A Dynamic and Adaptive Routing Algorithm for Wireless Sensor Networks
## 4 Simulation
### Metrics Overview
#### 4.1.2 Number of Messages Sent during the Set-up Phase  
The calculation of the number of messages sent during the set-up phase also depends on each routing algorithm.  
For \(AODV\), the number of messages for each node is calculated as the sum of all the messages sent for each \(RREQ\) message until the \(RREP\) message is received. The final number of messages of the whole network is obtained as the mean of the number of messages sent by all the nodes.  
For \(O\)-\(RPL\), the calculation is carried out by the \(DODAG\)_root_, counting the number of \(DIO\) messages sent, but discarding either \(DIS\) and \(DAO\) messages. As 10 different initializations with different \(DODAG\) roots are simulated for each network, the final number of messages is obtained as the mean number of messages of these 10 simulations.  
For \(DARAL\), all the control messages sent by each node (\(ASSOCIATION\_REQ\) and \(ASSOCIATION\_REP\)) are counted, since it is started until it changes its state to either \(CONNECTED\) or \(AWAITING\). Then, the final number of messages for the network is calculated as the mean number of messages sent by all the nodes.
## 3 Results and Discussion
### General Characteristics
#### 3.2.1 Heating Power  
One of the most common descriptors of how heat pumps work is the heating capacity. In this case, the figure below shows how the heating power varies at different compressor speeds, throttle positions, and volumes of the HP system. The heating power was evaluated by measuring the air flow rate and the temperatures before and after the condenser.  
Figure 12 shows that by adjusting the heat pump operation using conventional methods in the baseline case with a loop volume of V0, a range of supplied air heating capacity from 3.89 kW to 4.82 kW is achieved with a change of 0.93 kW. Increasing the volume of the HP loop by 17.0% (from V0 to V1), the heating power at the lowest speed and low flow rate  
Figure 11: Heating power vs. flow rate (manufacturers’ data).  
Figure 10: Heating power vs. flow rate (experimental data).  
is 3.02 kW. Increasing the speed and the flow rate of the expansion valve, the heat output gradually increases to 4.21 kW. There has been an increase in power, but fluctuations have also been observed, potentially due to the different speeds and flow rates. It can be seen that the power range (compared to the V0 case) has widened by 27.66% (from 0.93 to 1.18 kW), but the overall limits of the achieved power control range have decreased, compared to the baseline, from 3.02 kW to 4.21 kW. The trend remained unchanged with further increases in system volume. For V2, the loop volume increased by 26.4% to 6.01 dm\({}^{3}\), and for V3 by 34.4% to 6.40 dm\({}^{3}\) compared to the baseline (case V0). For case V2, the control range of the heating power increased by 65.15% (from 0.93 to 1.53 kW), and for case V3 by 69.62% (from 0.93 to 1.57 kW) compared to the baseline case. On the other hand, the overall limits of the achieved power control range decreased by 1.74 to 3.27 kW and by 1.25 to 2.82 kW for V2 and V3, respectively.  
It is noted that the use of additional controls by varying the volume of the heating circuit, in combination with conventional control measures, has allowed the heating power range to be extended, i.e., the difference between the maximum and the minimum power is achieved by varying the speed of rotation and the throttle capacity. This increase in the power range is as much as 0.7 times compared to the V0 case. It can be concluded that the volume expansion reduces the maximum heating power of the unit but allows it to be controlled over a wider range. A wider control range may be useful in cold climates during transition periods when a low HP output is required, which means that buffer capacity can be avoided. A buffer capacity is built in when the selected high-capacity HP cannot operate at low capacities, while the expansion of the volume reduces the power and increases the control range. This paper does not analyse the heat pump performance in the long term or in transient periods: this will be investigated in the next steps.
# Pharmaceuticals Removal from Wastewater with Microalgae: A Pilot Study
## 3 Results and Discussion
### Occurrence and Removal of Pharmaceuticals
As most studies indicate that the removal of pharmaceuticals by microalgae proceeds via sorption to microalgal biomass [21] and is therefore dependent on microalgal productivity [22], the concentration of nitrogen (N) and phosphorous (P) nutrients was monitored during the experiments (Figure 5 and Table S3, Supplementary Materials). N and P nutrients' concentrations in the inflow of the algal system varied considerably between both experiments. While no significant differences were observed between total nitrogen (TN), ammonium, and total phosphorous (TP) concentrations between both experiments, during Exp2 (autumn), the concentration of nitrates was significantly higher (average 19.8 mg NO\({}_{3}\)/mL) than in Exp1, conducted during the summer (average 5.07 mg NO\({}_{3}\)/mL; \(p<0.05\)). The concentration of biomass was also higher during the autumn experiment (average 83.2 mg/L) than during the summer experiment (average 31.7 mg/L; \(p<0.001\)). This could be linked to the higher concentration of nitrates detected in Exp2 (autumn) or the high temperatures registered in the summer (23.0-32.5 \({}^{\circ}\)C; Exp1) compared with those registered in autumn (between 18.0 and 24.5 \({}^{\circ}\)C; Exp2). Although light intensity was not measured, it is expected to be also higher in the summer. High light intensities and extreme temperatures, such as those registered in the summer in the South of Portugal, may decrease microalgal growth productivity, as they may hamper photosynthesis. Different microalgal species can also display different performances on the removal of pharmaceuticals. [PERSON] et al. tested 4 different microalgae against 10 antibiotics in synthetic wastewater under controlled laboratory conditions and found that _Haemaococcus pluvialis_ was the most efficient strain among those tested for antibiotic degradation. However, the affinity of microalgae to the antibiotics was dependent on the compounds' structure and different species exhibited distinct affinities to the different compounds [23]. Hence, the microalgal strain used can influence pharmaceuticals' removal. The current study did not use a monoalgal culture but rather relied on the development of naturally developed microalgal consortia. While this strategy may not be the most efficient for pharmaceutical removal in case the consortium lacks the most appropriate strain, microalgal natural consortia are usually more advantageous for wastewater treatment than monoalgal cultures. Being composed of different strains with distinct growth performances and demands in nutritional and environmental requirements, natural consortia can recover faster from culture collapse events, due to grazers, for example, and adjust easier to climate or wastewater composition changes than monoalgal cultures [15].
## 2 Wideband Antenna Designing
### Results of Unit Elements
#### 2.2.5 Radiation Efficiency  
Figure 9 shows the proposed antenna's predicted radiation efficiency. The antenna has a strong radiation pattern with a 26.5-42.7 GHz working bandwidth. At resonance frequencies of 30 GHz and 38 GHz, the antenna yields peak values of 86% and 90%, respectively, with peak values of around >82% at operating bandwidth.  
Figure 8: Surface current distribution of the proposed single element of antenna (**a**) 28 GHz and (**b**) 38 GHz.  
Figure 7: Radiation pattern and cross-polarization of a single element at (**a**) 28 GHz and (**b**) 38 GHz.
# Contour Mission Flight Planning of UAV for Photogrammetric in Hillside Areas
## 2 Methodology
### Grid-Type Flight Plan Method  
The grid-type mission is generally adopted in mountain terrain. The route is planned according to DEM calculations of flight level, which consider the average terrain elevation of the corresponding route. The analysis of flight height is based on the corresponding average elevation of the route position plus the shooting distance. The UAV collects images at the same height level and then rises to the next route height to capture images until the planned route is completed, as shown in Figure 1.  
When the UAV uses the same altitude to shoot vertically, if the area is flat, the UAV can keep the same altitude to take photos. However, when photographing in mountainous regions, if you still choose to keep the same altitude for photographs, it will affect the image shooting distance, resulting in an impact on the GSD and overlap, as shown in Figure 2. Figure 2a shows that when UAVs photograph at the same altitude, the shooting distance is shortened as the terrain rises (a > b), resulting in a significant difference in image resolution, and the ground image area that can be covered becomes smaller (c > d > e), as shown in Figure 2b, which reduces the image overlap. The above scenario shows that aerial photography at the same altitude will result in inconsistent image quality when taking pictures of rugged slope areas.
## 2 Structural Description and Numerical Modeling
### Modal Analysis  
As with other mechanical systems, the frame can be seen as a system with a constant of multiple degrees. This frame has the characteristics of an n-degree-of-freedom system, and the corresponding vibration control equation is represented by Equation (4):  
\[[M]\ddot{X}+[C]\dot{X}+[K]X=\{F\} \tag{4}\]  
From Laplace transformation:  
\[\Big{(}s^{2}[M]+s[C]+[K]\Big{)}\{X(s)\}=\{F(s)\} \tag{5}\]  
In Equations (4) and (5): \([M]\), \([C]\), and \([K]\) denote the mass, damping, and stiffness matrices, respectively; \(\left\{\ddot{X}\right\}\), \(\left\{\dot{X}\right\}\), and \(\{X\}\) represent the acceleration vectors, velocity vectors, and displacement vectors; \(\{F\}\) indicates the external force vector; \(\{X(s)\}\) corresponds to the displacement response in the Laplace domain. Such that \(s=j\omega\), the following is the case:  
\[\Big{(}-\omega^{2}[M]+j\omega[C]+[K]\Big{)}[\phi]\{q\}=\{F(\omega)\} \tag{6}\]  
In Equation (6), the orthogonality relation among the stiffness matrix \([K]\), mass matrix \([M]\), and vibration mode \([\phi]\) is satisfied; then, the vibration mode \([\phi]\) is reversible. After correlation transformation and independent decoupling the following is the case:  
\[\Big{(}k_{i}-\omega^{2}M_{i}+j\omega C_{i}\Big{)}q_{i}=\sum_{i=1}^{n}\phi_{ii} f_{i}(j=1,2,3,\ldots,n) \tag{7}\]  
Meanwhile, the response under \(l\) coordinate can be expressed as  
\[X_{l}=\sum_{i=1}^{n}\phi_{li}q_{i} \tag{8}\]  
The structural resonance of the fan is mainly manifested in the low-frequency domain, so the first six-order modal frequencies and corresponding modal vibration modes of the fan frame are extracted through modal analysis under prestress conditions, as shown in Table 3 and Figure 4. Notably, only the third-order 41.4 Hz falls within the shaft frequency excitation range among the first six modal frequencies.  
Figure 3: Static results: (**a**) stress cloud map; (**b**) deformation cloud map.  
The first and second vibration modes are caused by the cantilever structure of the connecting tube, which carries a large force and reduces its stiffness, resulting in the motor board and connecting tube translating together. The lateral constraint is weaker than the vertical constraint, so it exhibits lateral deformation first and then vertical deformation. Since both sides of the motor plate are constrained by the connecting tube, the third- and fifth-order vibration modes show the fluctuation state of the motor plate. It should be noted that the fan frame only fixes the mounting plate, so these six modes of vibration are caused by the cantilever structure.
## 2 Data
### Gps  
The GPS IWV product is obtained from the Geoforschungszentrum Potsdam (GFZ). A signal travel time is estimated from various GPS satellites to a receiver at the ground-based station. It provides information on the atmosphere, such as water vapor amount, along the travel path due to a signal delay that depends on the atmospheric state. In combination with meteorological observations of temperature and pressure at the GPS station, the part of the signal delay attributed to the water vapor amount, the so-called wet delay, can be related to IWV. About 40 to 50 observations along single signal paths within a 15 min time interval are used to estimate a representative IWV above the station [26]. The GFZ processes data of approximately 300 GPS stations within the German nation-wide network operationally and in near-real time (NRT) using the EPOS software [21]. Herein the necessary information on surface pressure and surface temperature is taken either from direct measurements at the station or interpolated from the smallest surrounding triangle of synoptic stations (see [27] for details). For more details on the method of IWV derivations from GNSS observations in general as well as the estimation of the IWV uncertainty, we refer to [28; 29], respectively. The IWV for each GPS station is provided with a temporal resolution of 15 min and an accuracy of 1-2 kg/m\({}^{2}\)[21; 30]. The spatial distribution of GPS stations in the German network is shown in the third panel of Figure 1.
## 3 Results
### Characterization and Semi-Quantification of Bioactive Compounds  
The semi-quantification of different BCs of _R. angustifolius_ extracts was carried out according to a semi-quantitative method focused on the main classes, starting from the UHPLC-QTOF data. The results are reported in Table S5, expressed as mg equivalents/g dry matter (DM). In particular, the main classes of metabolites were quantified using standard curves from pure compounds (purity \(>\) 98%). Thus, we quantified alkaloids, triterpenes, sesquiterpene lactones, and polyphenols classes, considering the three methods (HEA, MAC, and SOX) and the four extraction solvents (EA, EtOH, MetOH, and H\({}_{2}\)O). Afterward, a cumulative graph for each extraction method was created and reported in Figure 3A-C. The methanolic extract was the best one, allowing recovery of the highest content of BCs, ranging from 182.42 to 239.15 mg equivalents/g DM, highlighting HEA as the most efficient one. On the other hand, water was found to be the worst extraction solvent, recovering 132.10, 139.82, and 151.26 mg equivalents/g DM for MAC, HAE, and SOX, respectively (Figure 3A-C; Table S5).  
Regarding the different classes of compounds targeted, polyphenols and sesquiterpene lactones were efficiently extracted in methanol and using HAE, showing semi-quantitative values of 172.57, 59.44, mg equivalents/g DM (_p_ < 0.05), respectively (Figure 3A; Table S5). However, triterpenes and alkaloids were statistically abundant in EtOH extracts using the SOX method (7.59 mg equivalents/g DM; Figure 3C; Table S5) and EtOH-MAC (1.18 mg equivalents/g DM; Figure 3B; Table S5), respectively.  
Polyphenols are secondary metabolites widely abundant in plants and reported to exhibit health-promoting properties. The semi-quantitative results for the most important phenolic subclasses are shown in Table S6, expressed as mg equivalents/g DM. The cumulative graph of different polyphenols classes is provided in Figure 3D-F as a function of the different extraction methods tested. Overall, the methanolic extracts of HEA resulted in having a statistically higher quantity of the main phenolic classes (_p_ < 0.05), showing a greater recovery capacity for anthocyanins (6.96 mg eq./g DM), flavones (30.61 mg eq./g DM), flavonols(17.80 mg eq./g DM), and LMW phenolics (80.32 mg eq./g DM; Figure 3D; Table S6). Interestingly, stillbenes were efficiently extracted in the methanol solvent coupled to the SOX method (two-fold higher than the other techniques), reconvening 17.5 mg eq./g DM. Concerning lignans and phenolic acids, they were found to be more soluble in EA using MAC (49.68 mg eq./g DM) and SOX (15.92 mg eq./g DM), respectively (Table S6).
# An Unequal Clustering and Multi-Hop Routing Protocol Based on Fuzzy Logic and Q-Learning in WSNs
## 3 System Model
### Energy Model  
In FQ-UCR, the radio model from [35] is utilized, as depicted in Figure 2. The power consumption model for transmitting \(l\)-bit data packets from the transmitter to the receiver, separated by the distance \(d\), is calculated by Equations (1) and the energy consumption required to obtain \(l\)-bit of data by Equations (2):  
\[E_{TX}(l,d)=\begin{cases}l\times E_{dec}+l\times\varepsilon_{fs}\times d^{2}& \text{if \ }d\leq d_{0}\\ l\times E_{dec}+l\times\varepsilon_{mp}\times d^{4}&\text{if \ }d>d_{0}\end{cases} \tag{1}\]  
\[E_{RX}(l)=l\times E_{dec} \tag{2}\]  
Figure 1: The network model employed in this study.  
Figure 2: Radio model.  
The threshold \(d_{0}\) identifies whether the free-space model \(e_{fs}\) or the multipath model \(e_{mp}\) applies, with \(E_{elc}\) signifying the electronic circuitry's energy consumption. The calculation for \(d_{0}\) is expressed by the equation below:  
\[d_{0}=\sqrt{\frac{e_{fs}}{e_{mp}}} \tag{3}\]  
The multipath fading model is applied when \(d>d_{0}\), where the transmit energy usage \(E_{TX}(l,d)\) follows a quadratic relationship with \(d\). Conversely, the free-space model is used when \(d\leq d_{0}\). Therefore, when designing cluster routing algorithms, it is typically necessary to ensure that the transmission distance \(d\) between nodes does not exceed \(d_{0}\).
## 1 Introduction
### Estimating Lava Thickness  
To evaluate the thickness of lava flows emplaced between 1998 and 2007 at Piton de la Fournaise, we used two available Digital Elevation Models (DEMs). One is a 25 m resolution DEM produced by IGN from aerial photogrammetry in 1997 and the other is measured via airborne LiDAR surveying in 2008 with a 5 m resolution. It is worth noting that no eruptive events generating surface displacement occurred between the acquisition time of the 1997 DEM and March 1998, and between April 2007 and the acquisition time of the 2008 DEM. The two DEMs were first downsampled to the same grid posting. We then used the vertical difference between the two DEMs to estimate topographic height change, which reflects the thickness of total deposits. Given that lava flow is the main product of Hawaiian  
Figure 3: Nine extracted single lava fields coverage emplaced between March 1998 and April 2007. The dashed brown ellipse indicates the Eastern Flank Area (EFA) that is affected by both downward and eastward displacement during the study period. The red rectangle denotes southern part of the EFA where no lava fields emplaced between 1998 and 2007 but significant displacement occurred. F1 and F2 (black lines) represent the rough location of two structures inferred from this study.  
eruptions and composes much of the deposits, as at Piton de la Fournaise, and no independent data providing information about the discrepancies between the total deposit thickness and the lava thickness is available, we used the difference between the two DEMs to approximately represent the thickness of the lava flows. It is reasonable to some extent according to the previous study carried out by [PERSON] (16), who used the height change to represent the lava thickness at the Paricutin volcano, even though ash and pyroclastic are expected to account for up to 30% of the total deposit thickness. Additionally, the effects of erosion and redistribution of the less consolidated materials were not considered in this study. We used the extracted lava flow fields (Figure 3) as a mask to obtain the thickness of each lava field. The uncertainty of thickness of the lava flows was estimated to be 0.97 m by calculating the average standard deviation of the thickness in the areas without any emplacements of lava flows.