esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Future Work  
Several future works are planned for our approach. First, the building will be considered in its whole for the 3D reconstruction. Indeed, the 3D reconstruction is performed for each floor individually but some elements of a building are extended to several floors as for example facade walls. Outdoor point clouds will be used to improve the reconstruction of facade walls and to consider the detection of openings. Moreover, the combination of indoor and outdoor models will enable to integrate the building to models at larger scale such as city models.  
In parallel to the development of our approach, the potential use of radiometric information of point clouds (colour and intensity) was investigated for the detection of openings. Satisfying results  
Figure 17: Point clouds colourized with points-to-model signed distances: (**a**) For a surface. (**b**) For a volume.  
are obtained in condition to dispose of reliable radiometric information. The developed methodologies are for now not applicable to all point clouds. Works on the detection of openings will be deepened. In particular, the fact that facade walls contain windows and the fact that indoor walls contain doors will be exploited.  
As describe in Section 4, two quality indexes were defined and can be used to identify if some elements are not correctly reconstructed. In the future, a tool will be developed to automatically interpret these quality indexes in order to propose to the user a manual reconstruction of elements in case of errors. To do that, it is essential to keep the link between point clouds and the as-built BIM. Additionally, tolerances will have to be defined regarding simplification assumptions.  
In the context of BIM, it would also be interesting to define a specific acquisition protocol to integrate information which are not available in point clouds as for example ceiling heights when false ceilings are encountered or materials composing building elements. These information could facilitate the reconstruction and semantically enrich the as-built BIM.  
One should also notice that acquisition methods are constantly evolving. In this project, data issued from laser scanners were used but, considering the new techniques in photogrammetry and computer vision, point cloud of a building may be obtained by an automatic dense correlation of thousands images. Beside point cloud, the exploitation of images may allow extracting more semantic informations. Moreover, constraints may be imposed during the densification step to optimize the identification of walls, grounds and ceilings for example.  
A new trend for the acquisition of building environments is the combined use of several sensors. Multi-sensors systems are increasingly developed. The synchronized data issued from different sensors constitute a significant contribution for the automation of processes and can provide semantic information. For instance, the combination of laser scanning data and data issued from thermal cameras is notably studied in the context of as-built BIM creation.
# Creating a Regional MODIS Satellite-Driven Net Primary Production Dataset for European Forests
## 3 Results
### NPP across Elevational, Latitudinal and Longitudinal Gradients  
From Figures 3 and 4 as well as Table 2, we can see that the top-down MODIS EURO NPP estimates are consistent with the bottom-up terrestrial driven forest inventory NPP estimates at the European, regional and country level. Next, we investigated whether any patterns across gradients between MODIS EURO and NFI NPP may exist. For this purpose, we showed here \(\Delta\)NPP\({}_{\text{EURO}}\) for selected gradients, Elevation, Latitude and Longitude. We chose these gradients, since they have a strong effect on environmental and climatic conditions such as growing season length or weather patterns, but also on tree allometries and species composition, and are irrespective of country borders.  
We aggregated our results into classes to increase the readability and show Figure 5 the results for whole Europe (results on the different regions are available in Figures S2-S5 in the Supplementary Material). Images for additional gradients like tree age, tree height, MODIS land cover and dominant tree species are provided in Figures S6-S9 in the Supplementary Material.  
Figure 5: \(Cont.\)  
Grouping by elevation in Figure 5a does not indicate striking differences and shows, that the agreement between MODIS EURO and NFI NPP is consistent across the elevational gradients. At certain latitude and longitude classes however local discrepancies exist, which may correspond to the findings in Table 2 and Figure 4.
# Research on Pure Hydrogen Production Using a Fuel-Processing System Combined with a PSA System
## 4 PSA (Pressure Swing Adsorption) System
Based on the assumptions, the following mass balance and energy balance equations were set and used in the simulation [31; 32]. \[\frac{RT}{P}\rho\bigg{(}\frac{1-\varepsilon}{\varepsilon}\bigg{)}\frac{ \partial q_{i}}{\partial t}+\frac{\partial(uy_{i})}{\partial z}-D_{ax}\frac{ \partial^{2}y_{i}}{\partial z^{2}}=0 \tag{3}\]  
\[ \begin{array}{ll}\big{(}\varepsilon_{b}cC_{pg}+(1-\varepsilon_{b})\rho_{p}C _{ps}\big{)}\frac{\partial T}{\partial t}=&-\varepsilon_{b}cC_{pg}L_{\frac{ \partial T}{\partial z}}+K_{Z}\frac{\partial^{2}T}{\partial z^{2}}+(1- \varepsilon_{b})\rho_{p}\sum_{j=1}^{N}\left(\Delta H_{j}\frac{\partial q_{j} }{\partial t}\right)\\ &-\varepsilon\frac{2h_{ki}}{R_{in}}(T-T_{w})\end{array}  \tag{4}\]  
The mass balance equation can be written as Equation (3) and the energy balance equation is written as Equation (4). In the mass balance equation, the \(RT/P\) is the equation of state, \(\varepsilon\) represents the volume fraction in the mixture, \(\frac{\partial q_{i}}{\partial t}\) represents the rate of change over the time of component \(i\), \(\frac{\partial(uy_{i})}{\partial z}\) represents the mass flow in the vertical direction, and \(D_{ax}\) represents the diffusion rate of component \(i\). In the energy balance equation, \(\varepsilon\) is the volume fraction, \(C_{p}\) is the heat capacity, \(U_{z}\) is the speed of the fluid, \(\frac{\partial T}{\partial z}\) is the rate of temperature change in the \(z\) direction, \(K_{Z}\) is the effective axial thermal conductivity, and \(H_{j}\) is the reaction enthalpy. To calculate the number of gas molecules adsorbed per unit of adsorbent in the chamber, the extended Langmuir isotherm is applied, and it is given as the following equation. \[q=\frac{q_{mi}B_{i}P_{i}}{1+\sum_{j}^{N}B_{j}P_{j}} \tag{5}\]  
where \(q\) represents the number of moles of gas molecules adsorbed per unit mass of adsorbent and \(q_{m}\) represents the maximum adsorption amount of gas as a parameter. \(B_{i}\) is the Langmuir isotherm parameter of species \(i\) and \(P_{i}\) is the partial pressure of species \(i\). The amount of saturated adsorption is given as the following equation. \[q_{m}=a_{1}+a_{2}T \tag{6}\]  
\[B=b_{0}\exp\left(\frac{b_{1}}{T}\right) \tag{7}\]Parameters according to the adsorbent applied to the PSA system are shown in Table 3[44].
## 6 Results and Discussion
### Testing the Accuracy of the WSO-WOA Algorithm
#### 6.1.1 Cascading PI-PI Controller
While it approached the WSO-WOA's performance, the WOA still lagged behind, highlighting the subtle yet impactful differences in how these algorithms explore and exploit the search space. In contrast, the WSO algorithm showed the weakest performance, with its best fitness value recorded at 44.14 p.u. This represents a 14.1% increase over the result obtained by WSO-WOA, indicating a significant disparity in their optimization capabilities. This poorer performance suggests that WSO may struggle with more complex or multidimensional optimization problems, where more advanced hybrid algorithms like WSO-WOA can excel. Similarly, the GWO and SCA algorithms demonstrated less optimal performance than WSO-WOA, with increased fitness values of approximately 11.3% and 13.2%, respectively. Although both algorithms showed some promise, their results indicate a relative difficulty in achieving the same level of precision and optimization as the WSO-WOA. The WOA, however, had a comparatively smaller increase of 7.29%, making it the closest contender to WSO-WOA but still not matching its superior performance. Table 4 also highlights a trade-off associated with the WSO-WOA algorithm: it required more time to converge to  
Figure 9: Comparison of the convergence curves of the proposed WSO-WOA with other algorithms in tuning the cascading PI-PI controller. the optimal solution. The WSO-WOA took approximately 5.3 h to reach the best result, which is 1.97 times the time needed by the WSO, 1.83 times the time needed by the WOA, and 2.12 times the time needed by the SCA. This extended convergence time suggests that WSO-WOA is more accurate and demands greater computational resources. This trade-off between time and performance is crucial when selecting an optimization algorithm, particularly in real-time or resource-constrained applications. Nonetheless, the superior optimization results achieved by WSO-WOA may justify the additional computational time required.
## 4 Results and Discussion
### On the Training Set
#### 4.3.2 Shuffling Data  
When generating training examples from a GAN based model, shuffling the training data are a key important factor for obtaining an acceptable performance. Figure 15 illustrates the importance of data shuffling. The results presented in this figure were obtained with exactly the same RF-GAN configuration, the only difference being the way data are presented to the GAN for training. For non shuffled data, the model is simply not able to generate no matter the other initial conditions (weights).  
Figure 14: Learning curve of scenario RF-GAN for \(i=\)1, 2, 4, 6, 8, 10, 20, 60, 80, and 100% of the training set.  
Figure 15: The effect of shuffling input data for training a GAN based model: (**a**) with shuffling and (**b**) without shuffling.
## 3 Experimental Setup  
The proposed chatter identification method has been carried out in an ultra-precision machining machine, as shown in Figure 4.  
A copper bar is chosen as the work-piece and installed on the air spindle of the ultra-precision machining machine. The capacitive micro-displacement sensors with four measurement channels (Micro-sense DE 5300-013) are mounted on the handle of the apparatus to measure the vibration signals in three directions during cutting, the vibration signals are collected and then transmitted to the computer, which is used for data storage and signal processing. In addition, the power amplifier (PI, E-500), which has a criterion amplification factor 1060.1, is employed to amplify the driving signal. The Power PMAC controller generates the driving signals to drive the piezoelectric stack in the  
Figure 3: Flow chart of chatter identification in the three-dimensional elliptical vibration cutting (3D-EVC) process.  
apparatus. The apparatus of 3D-EVC is driven by the piezoelectric hybrid, which, given the sinusoidal excitation signal, can be expressed as:  
\[\left\{\begin{array}{l}x=A_{1}sin(\omega_{1}t+\varphi_{1})\\ y=A_{2}sin(\omega_{2}t+\varphi_{2})\\ z=A_{3}sin(\omega_{3}t+\varphi_{3})\end{array}\right. \tag{10}\]  
where, \(x\), \(y\) and \(z\) are the given sinusoidal excitation signal in X, Y and Z directions of tool tip corresponding to the coordinate system in machine tools, respectively. \(A_{1}\), \(A_{2}\) and \(A_{3}\) are the amplitudes of driving singles in three directions generated by three piezoelectric stacks, respectively. \(t\) represents the time. In addition, \(\varphi_{1}\), \(\varphi_{2}\) and \(\varphi_{3}\) are the angular frequency imposed on the piezoelectric stacks, and \(\omega_{1}\), \(\omega_{2}\) and \(\omega_{3}\) are the phase of driving signal along X, Y and Z directions, respectively.  
In order to compare the different cutting states during the cutting process, the value of spindle speed and the feed rate is fixed (90 rpm/min and 10 mm/min, respectively), the depth of cut is set to 5 \(\upmu\)m, 10 \(\upmu\)m and 20 \(\upmu\)m, respectively. The cutting process was slotting with the oil mist cutting fluid. To avoid the phenomenon of overcut or the larger cutting marks between the two periods, the amplitude of piezoelectric driving signal is 6 \(\upmu\)m. The detailed experimental conditions are listed in Table 1, and the main parameters of the material are listed in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Parameter** & **Value** \\ \hline \multirow{3}{*}{Work-piece} & Material & Copper (H62) \\  & Length (mm) & 50 \\  & Diameter (mm) & 12.7 \\ \hline \multirow{3}{*}{Diamond tool} & Nose radius (mm) & 0.2 \\  & Nominal rake angle (\({}^{\circ}\)) & 0 \\  & Nominal clearance angle (\({}^{\circ}\)) & 7 \\ \hline \multirow{3}{*}{Cutting parameter} & Depth of cut (\(\upmu\)m) & 5, 10, 20 \\  & Spindle speed (rpm/min) & 90 \\ \cline{1-1}  & Feed rate (mm/min) & 10 \\ \cline{1-1}  & vibration amplitude (\(\upmu\)m) & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental conditions.  
Figure 4: Experimental setup.
# A Congestion Control Strategy for Power Scale-Free Communication Network
## 4 Simulation Results
### Simulation Results for Global Dynamic Routing  
Under the shortest path routing, static local routing [23], improved efficient routing [24] and global dynamic routing strategy, the order parameter \(\eta\) vs. packet generation rate R is shown in Figure 10. The proportion of I-level packets is \(\lambda=0.2\). \(\alpha=-1\) in the static local routing and \(\beta=0.3\) in the improved efficient routing, which are the optimal values for the two routing strategies.  
In Figure 11, the critical packet generation rate under the shortest path routing is Rc = 25, under the static local routing is Rc = 31 and under the improved efficient routing is Rc = 38. Under the global dynamic routing, Rc reaches a very high value of 44, which is larger than the three routing strategies. As far as we know, the global dynamic routing has a larger network capacity and can reduce the occurrence of congestion.  
We can verify that the congestion occurs first at the hub nodes in the power communication network. When the packet generation rates are different, the total load of hub nodes changes with time, as shown in Figure 12. The abscissa is the time step t, and the ordinate is the total load of hub nodes n*(t) at time t; where \(\lambda=0.2\) and the hub node takes the node with k > 6.  
We know that the critical packet generation rate of global dynamic routing is 44 in Figure 11. The variation of the total load of the hub nodes with time step t is studied when R = 44 and R = 45. In Figure 12, when R = 44, the total load of the hub nodes fluctuates over time, but always near a certain value, the network is in a free-flowing state. When R = 45, the total load of hub nodes increases with time, and the network will enter the congestion state. When the packet generation rate exceeds the threshold, the packets are accumulated at the hub nodes over time, which indicates that congestion occurs first at the hub nodes. This is caused by the topology of the scale-free characteristic of the power communication network.  
Figure 11: The order parameter \(\eta\) vs. R under the four routing strategies.
## 5 Conclusions  
In this work, we applied the wireless network communication technology of an LPWAN to implement an intelligent forest Internet-of-things (IoT) management by taking the Fushan Botanical Garden in Taiwan as a real case. In the areas with 3G/4G, we set up NB-IoT-support sensors and continuously sent out their sensing data by using NB-IoT communication technology. In particular, an RSSI multi-hop for the signal repeat of the LoRa-support sensors was proposed to solve the problem of long-distance communication without 3G/4G. For the forest without electricity, we installed solar panels to supply electricity to the sensors and other equipment so that the sensing data on parameters such as light intensity, air pressure, ultraviolet intensity, and CO\({}_{2}\) would be transmitted to the LoRa gateway. By connecting the LoRa gateway to 3G/4G, the real-time sensing data were successfully transmitted to the web server. In order to avoid the problem of insufficient solar panels due to insufficient sunlight in the forest, we also connected each solar panel to a battery to store electricity.  
Figure 13: Realization of multiple LoRa-support solar micro-weather stations with repeat stations, and the communication technology.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Distance (km)**} & **RSSI at Mountain** & **RSSI at Mountain** & **RSSI at Mountain** \\  & **Edge 1** & **Edge 2** & **Edge 3** \\ \hline
0.6 & \(-63\) dBm & \(-67\) dBm & \(-64\) dBm \\ \hline
0.7 & \(-70\) dBm & \(-70\) dBm & \(-69\) dBm \\ \hline
0.8 & \(-73\) dBm & \(-75\) dBm & \(-74\) dBm \\ \hline
0.9 & \(-78\) dBm & \(-80\) dBm & \(-79\) dBm \\ \hline
1.0 & \(-80\) dBm & \(-79\) dBm & \(-83\) dBm \\ \hline
1.1 & \(-96\) dBm & \(-95\) dBm & \(-93\) dBm \\ \hline
1.2 & \(-121\) dBm & \(-122\) dBm & \(-120\) dBm \\ \hline
1.3 & \(-129\) dBm & \(-130\) dBm & \(-131\) dBm \\ \hline \hline \end{tabular}
\end{table}
Table 5: Correspondence between distance (_d_) and signal strength using the average RSSI from the LoRa-support solar micro-weather station at the mountain edge without rain.  
In the future, we will design a more comprehensive mathematical model that considers the location, number, and cost of weather stations, sensors, relay stations, gateways, and other equipment.  
Conceptualization, S.-T.C.; methodology, S.-T.C.; software, S.-T.C.; validation, S.-T.C., Y.-C.C. and Z.-Y.C.; writing--review and editing, S.-T.C., R.-J.Y. and M.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Microfluidic Collision and Experimentation  
The process of mechanically colliding \(H\). _lacustris_ cysts using a microfluidic system is illustrated in Figure 1. The microfluidic chip was fabricated via conventional photolithography based on a previously established protocol [41; 42]. Briefly, the chip mold was designed in AutoCAD(r)(Autodesk, San Francisco, CA, USA) with straight-line microchannels for cell lining and T-junction loop structures for cell collision ([PERSON] et al. [40]). The design was printed on photoresist negative film and used in photolithography. Silicon wafers were coated with SU-8 50 negative epoxy photoresist and pre-baked, exposed to ultraviolet light, post-baked, developed, washed, and cured to prepare the mold.  
The microfluidic device was fabricated by mixing polydimethylsiloxane (PDMS) with silicone elastomer hardener (10:1 ratio; Dow Chemical Co., Midland, MI, USA), pouring the mixture into the mold, and baking it at 70 \({}^{\circ}\)C for 2 h. After curing, the PDMS slab was peeled off, and inlets and outlets were punched using a sterile punch (Kai Medical, Seki, Japan). The slab was then irreversibly bonded to a glass coverslip via plasma treatment using a model PDC-32G device (Harrick Plasma, Ithaca, NY, USA) and further baked at 70 \({}^{\circ}\)C for 8 h to strengthen the bond.  
Mature red cysts of \(H\). _lacustris_ (approximately 0.2 g/L), harvested from 33-d-old seed cultures, were washed twice with fresh NIES-C medium before injection into the microfluidic device. The injection was performed using a Legato 101 syringe pump (KD Scientific, Holliston, MA, USA) equipped with a 6 mL Norm-ject(r) syringe (Henke-Sass, Wolf GmbH, Tuttlingen, Germany) and a sterile 23G needle (Korea Vaccine Co., Seoul, Republic of Korea), connected to the device inlet via Tygon(r) tubing (0.02-inch inner diameter; Saint Gobain PPL, Taipei, Taiwan). The outlet was similarly connected for cell collection.  
Mechanical collision intensity was adjusted by varying the flow rate (1.0 to 3.0 mL/min) and the number of T-junction loop passages (3 to 30). The treated cysts were cultivated photosynthetically in either 24-well plates (2 mL working volume) for 3 d or 250 mL Erlenmeyer flasks (120 mL working volume) for 30 d under the same conditions as the seed  
Figure 1: Schematic diagram of the microfluidic collision treatment applied to _Haematococcus lacustris_ cysts, followed by photosynthetic cultivation in well plates and flasks. The microfluidic chip, inspired by [PERSON] et al. [40], incorporates loops and sharp T-junctions to regulate cell collisions, with adjustable flow rates of 1.0 to 3.0 mL/min and 3–30 loops.  
flask cultures. Controls involved passing cysts through Tygon(r) (Saint-Gobain Performance Plastic's, Cleveland, ON, USA) tubing without microfluidic collision.
## 3 Results
### Standard Sample  
At last, the standard sample described in Section 2.2.3 was investigated with our OCT system. The thickness of the coating and the scattering layer was determined for the complete three-dimensional data set, and a heat map was generated. Figure 8 shows the microscopic image, the coating layer thickness, and the scattering layer thickness. There is a slight shift between the OCT measurement and microscopic image. This is because no additional markers were used in order to prevent any damage to the sample. In Figure 8b, a breach in the coating can be clearly seen (dark blue area). Moreover, Figure 8c shows the difference between pure metal and corroded metal. Pure metal can be recognized as dark blue color. With this sample, we were able to distinguish between metal and corrosion with \(\upmu\)m resolution without a priori knowledge of the distribution of the material. Furthermore, small breaches in the transparent protective coating could be displayed.  
Figure 8: **(a)** Microscopy image of a standard sample (sample 8); **(b)** heat map of the calculated layer thickness for the OCT measurement; **(c)** heat map of the calculated scattering layer thickness for the OCT measurement.
## 2 Materials and Methods
### Data Collection and Analyses
#### 2.3.3 Physiological Parameters  
Chlorophyll Fluorescence, Gas Exchange and Leaf Water Potential  
Chlorophyll fluorescence (Fv/Fm) was measured with a fluorometer (OPTI-SCIENCE, OS30p). Eclipses were placed on top of the leaves for twenty minutes to keep the leaves in the dark. This parameter was measured on well-developed leaves of the same rank (four repetitions per treatment) and is expressed in Fv/Fm; Fv: variable fluorescence (Fm-F0) and Fm: maximum fluorescence, F0 being fluorescence in the initial state when all the reaction centers of the PSII are open (oxidized quinones).  
Gas exchange (gs) was measured using a portable porometer (Leaf Porometer, Decagon Device, INC) on the fourth leaf from the apex as described by [PERSON] et al. [38]. Eight repetitions per treatment were performed. The values of this parameter are expressed in mmoles H\({}_{2}\)O m\({}^{-2}\) s\({}^{-1}\).  
The leaf water potential (leaf \(\Psi\)) was measured between 06:00 AM and 08:00 AM with the method of the pressure chamber developed by [PERSON] et al. [39] using a pressure chamber instrument (PMS Instrument Company, Albany, OR, USA).  
Photosynthetic Pigment Quantification  
The concentrations of chlorophyll a and b respectively, total chlorophyll and carotenoids were determined according to the method described by [PERSON] [40]. Fresh leaf tissue (100 mg) was crushed in 80% acetone. After centrifugation at 10,000\(\times\)_g_ for 10 min, the absorbance of the supernatants was recorded using a UV/visible spectrophotometer (UV-3100 PC spectrophotometer) at 480, 645 and 663 nm.