esa-sceva/satcom-chunk-collection huggingface dataset:
# Microalgae as Potential Sources of Bioactive Compounds for Functional Foods and Pharmaceuticals
###### Abstract  
Microalgae are an untapped source of bioactive compounds with various biotechnological applications. Several species are industrially produced and commercialized for the feed or cosmetic industries, however, other applications in the functional food and pharmaceutical markets can be foreseen. In this study, nine industrial/commercial species were evaluated for in vitro antioxidant, calcium-chelating, anti-tumoral, and anti-inflammatory activities. The most promising extracts were fractionated yielding several promising fractions namely, of _Tetraselmis striata_ CTP4 with anti-inflammatory activity (99.0 \(\pm\) 0.8% reduction in TNF-\(\alpha\) production in LPS stimulated human macrophages at 50 ug/mL), of _Phaocardium_ _Tricumatum_ with cytotoxicity towards cancerous cell lines (IC\({}_{50}\) = 22.3 \(\pm\) 1.8 ug/mL and 27.5 \(\pm\) 1.6 ug/mL for THP-1 and HepG2, respectively) and of _Paprolipy_ini sp. and _Skeleton_ sp. with good chelating activity for iron, copper and calcium (IC\({}_{50}\) = 0.047, 0.227, 0.0663 mg/mL and IC\({}_{50}\) = 0.055, 0.240, 0.0850 mg/mL, respectively). These fractions were chemically characterized by GC-MS after derivatization and in all, fatty acids at various degrees of unsaturation were the most abundant compounds. Some of the species under study proved to be potentially valuable sources of antioxidant, metal chelators, anti-tumoral and anti-inflammatory compounds with possible application in the functional food and pharmaceutical industries.  
anti-inflammatory activity; anti-tumoral activity; antioxidant activity; bioactive compounds; neuroprotective compounds; health benefits Article
## 2 UWB Patch Antenna Design
### Introducing EBG Structure into the Patch Antenna  
An EBG is established by placing a structure close to a patch to produce a field gap through the ground. The ground plane and the EBG structure are joined through a shorting pin called a via. It is a suitable option for creating low-profile, highly effective antennas [36]. An appropriate LC resonator can be used to represent a via-loaded metal patch connected to the ground. The parameters of the inductive (\(L_{in}\)) and capacitive (C) parts are described by Equations (8)-(10) [37].  
\[L_{in}=0.2h\left[\ln\left(\frac{2h}{r}\right)-0.75\right] \tag{8}\]  
\[C=\epsilon_{0}\,\epsilon_{r}\frac{w^{2}}{h} \tag{9}\]  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Parameter** & \(L\) & \(W\) & \(L_{1}\) & \(L_{2}\) & \(L_{3}\) & \(L_{4}\) & \(W_{1}\) & \(W_{2}\) \\ \hline Dimension & 31.0 & 24.0 & 17.7 & 0.5 & 11.5 & 1.5 & 10.25 & 3.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dimensions (mm) of the patch antenna.  
\[w_{0}=\frac{1}{\sqrt{LC}} \tag{10}\]  
In Equations (8)-(10), \(\epsilon_{0}\) and \(\epsilon_{r}\) denote permittivity parameters, while \(h\) and \(r\) denote the via's height and radius, respectively. Further, \(w\) and \(w_{0}\) are the EBG's width and the resonant angular frequency, respectively. The EBG structure could be observed as a mushroom-swollen surface, studied in [36], and was enhanced to produce M-EBG (see Figure 2). Figure 3 gives the structure and dimensions of the M-EBG. The M-EBG is portable, simple, and capable of generating decoupling effect in any common UWB array antennas when positioned in the feed line vicinity of the UWB micro-strip antenna.
# Design of Man-Machine Synergic Lunar Coring Device and Its Coring Dynamic Analysis
## 3 Man-Machine Synergic Lunar Coring Device Scheme Design  
### Design Requirements for Coring Devices  
Coring devices working on the lunar surface are subject to many limitations of the lunar surface environment. For example, overly complex devices are prone to malfunction during work, so coring devices, especially handheld coring devices, should be simple in structure and easy to operate. In the sampling process, the energy required for sampling the lunar regolith at different depths differs, so the coring device should adjust the impact frequency in real time to improve the sampling efficiency. The sampling methods applicable to lunar soil and lunar rockare different, and suitable sampling methods need to be selected for the sampling environment to improve the sampling success rate. When collecting deep samples, the length of the coring tubes needs to be increased. However, too-long coring tubes are not easily transported and operated by astronauts, which requires multiple sections of coring tubes to be interconnected. In particular, it should be noted that the lunar surface environment is very harsh and unsuitable for astronauts to work outside the landing module for long periods. Therefore, the coring device can be hand-held by the astronaut and quickly installed on the lander or rover for the autonomous operation of the coring.  
The man-machine synergic coring device should have the following characteristics:  
1. Astronauts' handheld coring device structure should be simple, easy to operate, and low power consumption.
2. The coring device should have extremely high stability to ensure that the coring missions are carried out safely and stably.
3. The coring device's impact frequency and energy can be adjusted according to the sampling depth.
4. The sampling method of the coring device (impact penetration and rotary drilling) can be changed according to the sampling environment.
5. The coring or drilling tube can be assembled freely to achieve depth sampling.
6. The handheld coring device is easy to assemble and disassemble and can be mounted on the lander or rover for autonomous operation.  
The design parameters of the man-machine synergic coring device were determined by considering the above design requirements and referring to the design parameters of ALSD, as shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Items**} & **Impact Frequency** & **Impact Energy** & **Rotational Speed** & **Single Tube Length** & **Inner Diameter of Coring Tube** & **Coring Rate** \\ \hline \hline Parameters & 0–20 Hz & 5–15 J & 0–300 rpm & 350 mm & 40 mm & \(\geq\)85\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Design parameters of the coring device.  
Figure 2: Impact penetration method diagrams. **(a)** Impact penetration coring; **(b)** Impact penetration coring after morphologic treatment; **(c)** Schematic diagram of the dual-mode coring method.
## 5 Discussion
### Comparison to Previous Work  
To the best of our knowledge, this study is the first attempt to analyze the user experience of an immersive VR experience based on a gesture-based UI to identify potential presence factors. The results of our study complement the presence factors identified in previous studies (see Table 2). However, these studies did not specifically focus on immersive VR with a gesture-based UI.  
With the recent proliferation of studies related to the metaverse, body gestures enabled by full-body tracking [56] have become an essential part of avatar-driven human-to-human interaction in immersive virtual environments [57]. In addition to synchronizing the full-body movement of the user with that of their avatar, haptic systems have been developed to offer touch-based interaction [58; 59]. For example, [PERSON] et al. [59] proposed a haptic hand-tracking system that accurately tracks the user's hands and provides sensations of vibration and heat to fingertips. Four Seasons is a single-user immersive VR experience that does not use haptic devices, but we expect that experiencing the natural environment together with friends while experiencing haptic feedback can enhance the sensations of immersion and presence [59].  
The emotional aspects of immersive VR experiences have recently piqued the interest of researchers [11; 60; 61; 62], some of whom have found linkages between emotions and presence [11; 61; 62]. Although our study did not specifically focus on the emotional aspects of immersive VR, unlike these previous studies, our results indicated emotional responses, with the participants expressing affective phrases such as \"it is fun\", \"I feel good\", \"it's amazing\", \"it is so beautiful\", and \"it is wonderful\". However, more research is needed to understand the connections between gesture-based UI and emotional engagement.  
The gorilla-arm effect is a known adverse effect of gesture-based UI using mid-air hand gestures [63]. It refers to the feelings of fatigue and discomfort caused by prolonged use of mid-air hand gestures. None of the participants in our study reported or showed signs of the gorilla-arm effect in our experiment. However, it is important to consider the potential effects of fatigue on the design of immersive VR experiences by choosing gestures that are less likely to cause fatigue [63], keeping the gesture-based interaction time relatively short, and allowing the user to rest their hands at will.
# A High-Conversion Ratio Multiphase Converter Realized with Generic Modular Cells
## 3 Steady State Analysis
### Inductors' Currents and Balancing  
For simplicity, it is assumed that the converter is loaded with a very light load at the output. The load current \(I_{\rm out}\) is delivered from all three phases and equals to the sum of the average inductors' currents. This relationship can be expressed by the following equation:  
\[I_{\rm out}=I_{L1}+I_{L2}+I_{L3}. \tag{9}\]  
The average value of each of the inductor's currents at a steady state can be calculated using the fact that the charge balance is achieved for each of the capacitors [54]. For example, the charge delivered to \(C_{2}\) during state III must be equal to the charge consumed from \(C_{2}\) (by \(C_{3}\)) during state V. Thus, charge equations can be summarized as follows:  
\[\begin{array}{l}\rm Q_{C1},\ \frac{1}{2}I_{L_{2}}\frac{D_{2}}{f_{s}}\bigg{|}_{ \rm charge}=\ I_{L}\frac{D_{1}}{f_{s}}\bigg{|}_{\rm discharge},\\ \rm Q_{C2},\ \frac{1}{2}I_{L_{2}}\frac{D_{2}}{f_{s}}\bigg{|}_{\rm charge}=\ I_{L} \frac{D_{1}}{f_{s}}\bigg{|}_{\rm discharge},\\ \rm Q_{C3},\ I_{L_{3}}\frac{D_{1}}{f_{s}}\bigg{|}_{\rm charge}=\frac{1}{2}I _{L_{2}}\frac{D_{2}}{f_{s}}\bigg{|}_{\rm discharge}.\end{array} \tag{10}\]Solving the system using Equation (9) yields the average inductors' currents:  
\[\begin{array}{l}I_{L1}=\frac{D_{2}D_{3}}{D_{2}D_{3}+D_{1}D_{2}+2D_{1}D_{3}}I_{ \text{out}},\\ I_{L2}=\frac{2D_{1}D_{3}}{2D_{1}D_{3}+D_{1}D_{2}}I_{\text{out}},\\ I_{L3}=\frac{D_{1}D_{2}}{D_{2}D_{3}+D_{1}D_{3}+2D_{1}D_{3}}I_{\text{out}}.\end{array} \tag{11}\]  
Therefore, in the most straightforward private case where duty cycles are equal, \(D_{1}=D_{2}=D_{3}=D\), the three-phase converter average inductor currents become \(I_{L1}=I_{\text{out}}/4\), \(I_{L2}=I_{\text{out}}/2\) and \(I_{L3}=I_{\text{out}}/4\). It should be mentioned that phase 2 carries double the current compared to phases 1 and 3. This is a result of both capacitors \(C_{1}\) and \(C_{3}\) discharging at state III, through inductor \(L_{2}\). The inductors' current balance between phases can be achieved by setting the duty cycle of phase 2 to be half of the duty cycle of phases 1 and 3. The duty cycles in this case are summarized in (12), and the voltage ratio of the converter becomes \(M=D_{2}/6=D_{1}/3\).  
\[D_{1}=D_{3}=D_{2}/2. \tag{12}\]
## 3 Satellite Geometries
### Uplink Satellite Geometry for DoA Estimation
Therefore, \(\mathrm{d}_{\mathrm{SPES}}\) in Equation (2), is determined by applying the cosine rule for plane triangles to the triangle of Figure 1b as below:  
\[\mathrm{d}_{\mathrm{SPES}}=\sqrt{\mathrm{R}^{2}+\mathrm{a}_{\mathrm{SPO}}^{2} +2\mathrm{R}_{\mathrm{SPO}}\cos\mathrm{b}}\, \tag{4}\]  
Figure 1: Uplink satellite geometry for DoA estimation using a geographic (i.e., spherical) coordinate system: (**a**) spherical triangle with angles “abc” as arcs of great circles, (**b**) plane triangle “OESSP” to determine the elevation angle (EI) of the Earth station (ES) and slant distance (\(\mathrm{d}_{\mathrm{SPES}}\)) for different satellite systems. Finally, the angle \(\theta\) in Figure 1b is given by:  
\[\theta=180^{\circ}-(\alpha+\mathrm{b}), \tag{5}\]  
where angle \(\alpha=90^{\circ}+\mathrm{El}\), and \(\theta\) is the DoA of the GEO satellite. For the uplink communication of a LEO satellite, the same geometric configurations as those used for a GEO satellite, shown in Figure 1, is applied. To determine the El, \(\mathrm{d_{SPS}}\), and \(\theta\) of the LEO satellite system, Equations (1)-(5) are employed. However, as the altitude of the LEO satellite system ranges from 180-2000 km, the calculation of \(\mathrm{d_{SPS}}\) for the LEO satellite system requires a different distance value, \(\mathrm{a_{SPO}}\), from SP to O, compared to the GEO satellite system. Thus, \(\mathrm{a_{SPO}}\) is set to 1500 km for the LEO satellite system. The illustration of the uplink satellite geometry for DoA estimation in different satellites is shown in Figure 2.
# Utilization of Phosphogypsum and Sediment in Subgrade Material for Pavement Construction
## 3 Results  
### Physicochemical and Thermal Characterization of Sediment Samples Treated with Phosphogypsum  
#### 3.1.1 Pseudo-Total Metal Content  
When determining the pseudo-total metal concentrations presented in Table 3, it is observed that zinc is present in the highest concentration in the initial sediment, while arsenic is found in the lowest concentration. The concentrations of metal were first adjusted to standard sediment levels and then classified according to national regulations [25]. The sediment quality was assessed based on the parameters that fell into the worst category as predicted by the model.  
From the results in Table 3, it can be seen that cadmium, copper, and zinc are classified as belonging to the fourth class of sediment--extremely polluted--based on the concentrations of these specific metals. In contrast, the sediment is classified as third class--heavily polluted--regarding nickel concentrations. According to the \"Official Gazette of RS\", no. 50/12 [25], sediments categorized as fourth and third class are considered polluted, necessitating treatment before their disposal in the environment. Meanwhile, chromium, lead,  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Analysed Metals** & **Cr (mg/kg)** & **Ni (mg/kg)** & **Cu (mg/kg)** & **Zn (mg/kg)** & **As (mg/kg)** & **Cd (mg/kg)** & **Pb (mg/kg)** \\ \hline Sediment of Bogej & 304.9 & 58.42 & 345.1 & 1415 & 18.28 & 18.65 & 177.9 \\ Corrected value & 287.02 & 53.63 & 307.4 & 1272 & 16.60 & 16.35 & 163.7 \\ Target value & 100 & 35 & 36 & 140 & 29 & 0,8 & 85 \\ Remedial value & 380 & 210 & 190 & 720 & 55 & 12 & 530 \\ Classification & 1 & 3 & 4 & 4 & 0 & 4 & 1 \\ \multicolumn{8}{c}{Final classification: 4} \\ \multicolumn{8}{c}{Class 0—natural sediment background; class 1—slightly polluted sediment; class 2—moderately polluted sediment; class 3—heavily polluted sediment; class 4 (4+)—extremely polluted sediment} \\ \hline \end{tabular}
\end{table}
Table 3: Pseudo-total metal content in sediment sample.  
and arsenic fall into the first and zero classes--slightly polluted and natural background sediments, respectively.
## 2 Network Model
### STDP Algorithm  
The important part where neurons come into contact with each other is called synapse, which can be used to transmit information. Among them, presynaptic neurons emit information and post-synaptic neurons receive information. The property of a synapse to continuously change and adjust itself to suit the needs of the body under various stimuli and influences is called synaptic plasticity. The STDP algorithm establishes associations between neural stimuli and modulates synaptic connection weights. Its weight learning mechanism can be expressed as,  
\[\Delta w=\sum_{t_{pre}}\sum_{t_{post}}w(t_{post}-t_{pre}) \tag{4}\]  
\(t_{post}\) represents the time when a post-synaptic neuron generates a pulse, and \(t_{pre}\) represents the time when a pre-synaptic neuron generates a pulse. The regulation of synaptic connection weights is specifically described by STDP rules, if pre-synaptic neurons are stimulated before post-synaptic neurons, it will lead to long-term potentiation of synapses (LTP), that is, the weight of the connection between the synapses of two neurons will increase, otherwise it will lead to long-term depression (LTD) of the synapse, that is, the weight of the connection between the synapses of two neurons will decrease. The conventional STDP time function is as follows:  
\[\begin{array}{l}w(\Delta t)=A_{+}\exp(\frac{\Delta t}{\tau_{+}}),\Delta t \geq 0\\ w(\Delta t)=A_{-}\exp(\frac{\Delta t}{\tau_{-}}),\Delta t<0\end{array} \tag{5}\]  
Among them, \(\Delta t\) represents the time when the post-synaptic neuron generates pulses, \(A_{+}\) and \(A_{-}\) represent the range of weight adjustment, \(\tau_{+}\) and \(\tau_{-}\) represent the constant of different neuron models.  
The parameter values in the STDP algorithm are shown in Table 2, and the STDP algorithm curve chart obtained under the parameters is shown in Figure 5. It can be seen from the figure that when \(\Delta t<0\) there is a causal connection between the pre-neuron and the post-neuron, the synapse weight increment increases exponentially, and the shorter the pulse time interval is, the closer the connection is, and the more the corresponding synapse weight increases. When \(\Delta t\geq 0\) there is an inverse causal connection between the front and  
\begin{table}
\begin{tabular}{c c} \hline
**Image** & **Average Number** \\ \hline “0” & 4.33 \\ “1” & 2.667 \\ “2” & 5.667 \\ “3” & 4.667 \\ “4” & 5 \\ “5” & 4 \\ “6” & 5 \\ “7” & 3 \\ “8” & 4.667 \\ “9” & 4.33 \\ \hline \end{tabular}
\end{table}
Table 1: Average Number of Pulses.  
back neurons, and the weight increment decreases exponentially, and the shorter the time difference, the more the weight decreases.
###### Abstract  
With few measurement sites and a great need to validate satellite data to characterize the exchange of energy and carbon fluxes in tropical forest areas, quantified by the Net Ecosystem Exchange (NEE) and associated with phenological measurements, there is an increasing need for studies aimed at characterizing the Amazonian environment in its biosphere-atmosphere interaction, considering the accelerated deforestation in recent years. Using data from a flux measurement tower in the Caixuana-PA forest (2005-2008), climatic data, CO\({}_{2}\) exchange estimated by eddy covariance, as well as Gross Primary Productivity (GPP) data and satellite vegetation indices (from MODIS), this work aimed to describe the site's energy, climatic and carbon cycle flux patterns, correlating its gross primary productivity with satellite vegetation indices. The results found were: (1) marked seasonality of climatic variables and energy flows, with evapotranspiration and air temperature on the site following the annual march of solar radiation and precipitation; (2) energy fluxes in phase and dependent on available energy; (3) the site as a carbon sink (\(-569.7\pm 444.9\) gC m\({}^{-2}\) year\({}^{-1}\)), with intensity varying according to the site's annual water availability; (4) low correlation between productivity data and vegetation indices, corroborating data in the literature on these variables in this type of ecosystem. The results show the importance of preserving this type of environment for the mitigation of global warming and the need to improve satellite estimates for this region. NDVI and EVI patterns follow radiative availability, as does LAI, but without direct capture related to GPP data, which correlates better with satellite data only in the months with the highest LAI. The results show the significant difference at a point measurement to a satellite interpolation, presenting how important preserving any type of environment is, even related to its size, for the global climate balance, and also the need to improve satellite estimates for smaller areas.  
**Keywords: eddy covariance; enhanced vegetation index; gross primary production; normalized vegetation index**
# Embracing the Future Internet of Things +
## 3 IoT Platform Capabilities  
### Service-Defined Data Analytics  
Data analytics in the future IoT needs to adapt to the intrinsic distributed nature of IoT. Thus, the data is processed at every layer of IoT (i.e., device, edge and cloud). This is not only for scalability reasons (such as minimizing network bandwidth by filtering or aggregate useless and redundant data) but also for taking into consideration the high dynamism of IoT infrastructure topology (e.g., new devices joining the IoT system, mobile devices changing their physical location, battery powered devices temporary unavailable because of recharging, intermittent communication channel to devices). Moreover, privacy requirements steer the decision about the allocation of processing tasks since it is always wiser to execute privacy preserving data processing as closest as possible to the generation of the data. The needed analytics are leveraged by IoT services with heterogeneous requirements such as geographical scope of data analytics [2] or Quality-of-Service [11]. The data analytics framework of the future IoT transforms data analytics requests to multiple atomic tasks to be instantiated and orchestrated among the edge and cloud layers depending on the service requirements.  
As an example of a service-defined data analytics, we consider crowd mobility analytics [12] that estimates the crowd levels within an area and the flow of people moving between areas. Data sources (e.g., Wi-Fi sniffers, bluetooth beacons, ambient sensors such as temperature sensors) generate observation of the real world. Edge nodes act as gateways, collecting the observations and then sending them to cloud nodes. With the concept of service-defined data analytics, the processing is decomposed in multiple tasks. Thus, the needed analytics can be divided into two categories, the first for lightweight tasks that can run on the edge nodes and the second for power-consuming tasks to be executed on cloud nodes. In the first category, for our example, we can include the _stream processing_ part which comprehends a filtering task that filters redundant or unnecessary data, and a privacy preserving task that anonymizes the observations (important for Wi-Fi monitoring). In the second category, continuing the example, we can include the _batch processing_ part where historical data is aggregated and patterns are detected.  
Figure 3: IoT-enhanced autonomous driving. [10]
## 3 Knowledge-Guided Parallel Hybrid Local Search Algorithm
### Knowledge-Guided New Solution Construction  
Constructing new solutions is paramount in local search algorithms as it directly influences the algorithm's performance and search effectiveness. Therefore, selecting suitable methods for new solution construction based on the specific problem context is crucial. Relying solely on random generation methods would inevitably lead to low search efficiency. Hence, we embrace knowledge-based new solution construction methods, employing three heuristic rules to generate new solutions. The following three heuristic rules were used in the knowledge-guided solution construction strategy.  
* Maximum Profit From the perspective of maximizing benefits, the satellite removes the task with the slightest benefit in the current solution. Then, the task with the highest current benefit is selected from the task pool and inserted into the schedule, thereby constructing a new solution.
* Unit Profit Construct the new solution by removing the \(k_{n}\) tasks with the most minor unit gains from the current solution. Then, tasks with more considerable unit gains are selected from the current task sequence and inserted into the solution. The unit gain is defined as the gain of a problem divided by its observation time.
* Conflict Level Construct the new solution by removing the \(k_{n}\) tasks with the highest conflict degrees from the current solution. Then, tasks with lower conflict degrees from the task pool are inserted into the solution. The conflict degree is defined as the conflict time between a current task's visible time window and other tasks' visible time windows.