esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods  
### Description of the Study Sites  
Ethiopia is located in eastern Africa, geographically extending from 3\({}^{\circ}\) to 15\({}^{\circ}\) north latitude and from 33\({}^{\circ}\) to 48\({}^{\circ}\) east longitude. It is known for its topographic diversity, stretching from the lowest Danakil depression 125 m below sea level to the highest peak of the Simien Mountains, which is over 4500 m above sea level. The great East African rift valley that runs from northeast to southwest divides the country into north, northwest, and southwest highlands and the western lowlands on one side, and the eastern and southeastern highlands and the associated lowlands on the other side [32]. This topographic diversity favored the country to have a wide range of climate and a diverse flora and fauna, with a considerable amount of it being endemic [33].  
For this study, seven sites were identified to represent the major biomes of the country, as well as their tree gain or loss conditions (Table 1). The selection of sites was subjective in order to have sites covering a range of forest conditions. The size of each site was 2052 km\({}^{2}\) (36 km \(\times\) 57 km). The major biomes were used in activity data compilation for FRL submission to the UNFCCC, and stratification during the national forest inventory planning and implementation [27]. The major biomes include (1) Dry Afromontane: includes undifferentiated Afromontane forest; dry single dominant Afromontane forest of the Ethiopian highland; Afromontane woodland, wooded grassland, and grassland. In addition, transition between Afromontane vegetation and _Acacia-Comniphora_ bushland on the eastern escarpment, as well as Ericaceous and Afroalpine belts, are included. (2) Moist Afromontane: comprises mainly primary or mature secondary moist evergreen Afromontane forest. Also contains edges of moist evergreen Afromontane forest, bushland, woodland, and wooded grassland, as well as transitional rain forest. (3) _Combretum-Terminalia: Combretum-Terminalia_ woodland and wooded grassland is a major component. Furthermore, it includes wooded grassland of the western Gambela region. (4) _Acacia-Commiphora: Acacia-Commiphora_ woodland and bushland proper; Acacia wooded grassland of the rift valley as well as desert and semi-desert vegetation [32]. Accordingly, dry Afromontane forests cover the north, central, and eastern highlands of the country; moist Afromontane forests dominate the southwest and south-central areas. _Combretum-Terminalia_ covers the northwest, west, and southwest lowlands; _Acacia-Commiphora_ includes the northeast, east, and south lowlands of the country, while the rest that is categorized as others included water bodies and wetlands (Figure 1).
## 3 Simulation Experiments
### The Simulated Observations of GP along the TS Orbit
Furthermore, the cumulative effect of many varied, minor errors will behave more similarly to white Gaussian noise. Given Equations (8) and (11), we can generate the noise signals \(e_{all}(t)\) term in Equation (7) based on the characteristics and magnitudes of the error sources at any given time. Therefore, we obtain a set of \"observed\" values, which forms a time series of the TS's GP \(\hat{\phi}_{T}(t)\), as illustrated on the left side of Equation (7). Considering that the TS orbits the entire Earth in approximately 30 days, these values represent the GP at different time points along the TS's orbit. Consequently, we have a set of values \(\hat{\phi}_{T}(x,y,z)\) correlated with orbit data. Upon 30 continuous days (equivalent to 720 h) of observation, a total of 2,592,000 GP points, denoted as \(\hat{\phi}_{T}(x,y,z)\), were distributed over the TS sphere encompassing the Earth.
# The SSP-Tree: A Method for Distributed Processing of Range Monitoring Queries in Road Networks
## 3 The Proposed Method
### Vicinity Region-based Communications and Query Processing
#### 3.3.1 Server-Side Tasks  
The server performs three main tasks: query registration, region assignment, and query result update.  
* **Query registration:** When a new query \(q\) is issued by a client, the server inserts \(q\) into QT and invokes ComputeSeg (see Algorithm 1) to compute the relevant query segments of \(q\). Then, for each query segment \(qs\) generated by ComputeSeg, the server (i) invokes Insert (see Algorithm 3) to insert \(qs\) into the SSP-tree and (ii) broadcasts the InsertQS\((qs)\) message. In case that a leaf node \(\hat{N}\) of the SSP-tree is split into \(N_{left}\) and \(N_{right}\), the server broadcasts the SplitRegion\((\hat{N},N_{left},N_{right})\) message. On the other hand, when an existing query \(q\) is terminated by a client, for each relevant query segment \(qs\) of \(q\), the server (i) invokes Delete (see Algorithm 5) to delete \(qs\) from the SSP-tree; (ii) broadcasts the DeleteQS\((qs)\) message; and finally (iii) deletes \(q\) from QT. In case that two leaf nodes \(N_{left}\) and \(N_{right}\) are merged, the server broadcasts the MergeRegion\((N_{left},N_{right},\textit{qs\_set}(_{left+right}))\) message, where \(\textit{qs\_set}_{(left+right)}\) is the combined set of query segments inside \(N_{left}\) and \(N_{right}\).  
* **Region assignment**: When the server receives the RequestVR(\(o.p,o.cap,N_{old}\)) message from a moving object \(o\), where \(o.p\), \(o.cap\), and \(N_{old}\) denote the point of \(o\)'s current location, \(o\)'s capability, and \(o\)'s previous vicinity region, respectively, it first checks if \(o\) leaves each original query segment stored in \(N_{old}.FL\). If so, the server updates the result of the corresponding query when necessary. Then, the server invokes Search (see Algorithm 2), after which it sends the AssignVR(\(N_{new},qs\_set\)) message to \(o\), where \(N_{new}\) is \(o\)'s new vicinity region and \(qs\_set\) is a set of query segments inside \(N_{new}\).
* **Query Result Update:** When the server receives the UpdateResult(\(o.p,qs\)) message from a moving object \(o\), it visits QRT to find the relevant queries of \(qs\). Then, the server checks each \(qs\)'s relevant query \(q\) if \(dist_{N}(q.p,o.p)\leq q.d\). If so, the server inserts \(o\) into the result of \(q\) (if it is not already there). On the other hand, if \(dist_{N}(q.p,o.p)>q.d\), the server removes \(o\) from the result of \(q\) (if it is there).
## 2 Materials and Methods
### Aerosol Jet Printing  
To demonstrate the possibility of the application of formulated ink in printed electronics, we patterned conductive lines and microheaters on alumina substrates and low-temperature co-fired ceramic (LTCC) membranes with the use of aerosol jet printing technology. An AJ 15 XE commercial aerosol jet printer (Neotech AMT GmbH, Nuremberg, Germany) utilizing the aerodynamic focusing technique was used for this purpose. In this technology, the ink is deposited onto the substrate in the form of aerosol particles generated in the atomizer. The printing resolution (lower size limit of printed features) is determined by the aerosol stream diameter at the substrate surface, which is controlled by a ratio between the sheath gas flow and the aerosol flow in the nozzle (focusing ratio) and by a nozzle orifice diameter.  
Figure 1 presents a stencil of a microheater designed in AutoCAD software, which was then compiled by the firmware of the aerosol jet printer. This stencil was used to print a series of microheaters on a 20 \(\upmu\)m thick LTCC membrane covering a 100 \(\upmu\)m thick LTCC wafer comprising holes with a diameter of 4 mm. The stencil was positioned with respect to the hole so that its narrowest part would be located in the central area of the hole in order to minimize the power consumption of the microheater when heated by a current.  
The average dosage of deposited ink (mass of the ink per unit area of the covered surface) was tailored so as to provide an average thickness of the printed features in the final (annealed) form in the range of 0.4-0.7 \(\upmu\)m, thus exceeding the root mean square (RMS) roughness of the substrates used by a factor of more than 3. When printing the narrowest part of a microheater, the average dosage was controlled by varying the atomizer gas flow, printing speed, substrate temperature and number of consecutive passes of an aerosol jet along a given path. When printing the arms of a microheater, the average dosage was additionally controlled by varying the distance between neighboring paths of the aerosol jet. In the performed experiments, the following operating conditions were used: gas flow in the pneumatic atomizer of 700 sccm, focusing ratio of 2, nozzle orifice diameter of 150 \(\upmu\)m, printing speed of 100 mm/min and a substrate temperature of 100 \({}^{\circ}\)C.
## 3 Results and Discussion
### Neutron Diffraction Analysis  
Neutron diffraction can reveal _hkl_ planes within a polycrystalline material. In the case of a Li-ion/Li-po battery, this becomes more complex due to the wide range of materials used for anode, cathode, electrolytic, and separator material. Figure 6 shows the neutron diffraction spectra of a charged and discharged, fresh and worn Li-ion battery taken from 0.05-0.25 nm at the VULCAN instrument (ORNL, SNS).  
This neutron diffraction experiment was expected to show qualitatively how the components within a Li-ion battery can change during the cycling process. In all spectra, the largest peak at 0.181 nm corresponds to a Cu (002) peak that is not changing in the battery, hence the high intensity for both the charged and discharged states. For the fresh battery, the most observable peak differences can be observed at 0.157 nm, 0.168 nm, 0.176 nm, 0.185 nm, and 0.214 nm. At 0.157 nm, the Li\({}_{0.5}\)CoO\({}_{2}\) (112) peak appears for the charged state in both the fresh and the worn battery. The peaks at 0.185 nm and 0.214 nm are related to Li\({}_{0.75}\)CoO\({}_{2}\) (015) and Li\({}_{x}\)C\({}_{6}\) (110), respectively. When the battery is charged (blue spectrum), both peaks are prevalent and when the battery is discharged (black spectrum), the peaks are no longer present, indicative of lithium de-intercalation. The peaks at 0.168 nm and 0.176 nm represent graphite (004) and LiC\({}_{12}\) (004), respectively. At 0.176 nm, when the battery is charged, smaller lithium intercalated components such as LiC\({}_{12}\) exist. When the battery is discharged, lithium de-intercalates within the graphite and thus the peak at 0.168 nm appears.  
In comparing the fresh battery to the worn battery spectra, several changes can be observed. First, at 0.157 nm, the 112 peak for Li\({}_{0.5}\)CoO\({}_{2}\) is present in both the charged and discharged states. This implies that the lithium is still intercalated in theCoO\({}_{2}\) structure despite the battery being discharged. The Li\({}_{0.75}\)CoO\({}_{2}\) 015 peak at 0.185 nm and the Li\({}_{x}\)C\({}_{6}\) 110 peak at 0.215 nm are no longer present. Although the battery can still undergo charging/discharging, this suggests that lithium is not moving within the battery as it once  
Figure 5: The **top left** image shows a diffraction plot of a worn and fresh battery at 0.318 nm. The **top right** image shows the difference image of charged—discharge state a 0.318 to 0.324 nm. Bottom images are Bragg edge plots from 0.30 nm to 0.44 nm of the worn (**left**) and fresh (**right**) batteries. The (+) symbol represents when the battery is charged while the (\(-\)) symbol represents the discharged battery.  
did. Lithium can be trapped in a non-electrochemically or non-crystalline active phase within the battery such that lithium intercalation cannot be fully achieved as in the first charge/discharge cycle. Also observable is the absence of the graphite 004 peak at 0.168 nm and the increase in LiC\({}_{12}\) 004 signal at 0.176 nm for the discharged battery.
## 3 Results
### Biological Activities  
#### 3.2.1 Determination of Antioxidant Activity  
The antioxidant activity revealed significant differences (\(p\) < 0.05) between ethanolic extracts of the studied species. The total antioxidant capacity (TAC) was determined using the phosphomolybdenum method. The most important TAC was shown in _C. villosa_ (15.33 \(\pm\) 0.39 mg GAE/gDW) and _P. latifolia_ (20.16 \(\pm\) 0.32 mg GAE/gDW).  
Indeed, in this part of the study, two tests were used: DPPH and FRAP. All the tests are based on electron or hydrogen transfer to assess the free radical scavenging activity. Results showed significant differences (\(p\) < 0.05) between ethanolic extracts (Table 1). Since IC\({}_{50}\) is in inverse proportion to the antioxidant capacity to antioxidant capacity, the lowest IC\({}_{50}\) value corresponds to the most potent antioxidant. Our results showed that the highest DPPH free radical reduction activity in EE species was detected for _C. villosa_, and the lowest were those corresponding to _P. lentiscus_ and _E. multifora_. Similarly, ferric reductase activity exhibited the same pattern compared to that of DPPH. _P. lentiscus_ and _E. multifora_ showed the greatest ability to reduce ferric ions (Fe\({}^{3+}\)).  
Figure 1: Total phenols (mg GAE/gDW; (**A**)), flavonoids (mg CE/gDW; (**B**)) and tannins (mg CE/gDW; (**C**)) content in P1 (_Pistacia lentiscus_), P2 (_Rosmarinus officinalis_), P3 (_Erica multiflora_), P4 (_Calicotome villosa_) and P5 (_Phillynea latifolia_) ethanolic extracts. Means of three replicates are reported (\(\pm\)SD). Significant differences are detected in the bars (\(p<0.05\)), Duncan’s test. The letters (a–e) denote significant (\(p<0.05\)) difference between means.  
\begin{table}
\begin{tabular}{l c c c} \hline  & **TAC (GAE/gDW)** & **DPPH (IC\({}_{50}\) mg/mL)** & **FRAP (EC\({}_{50}\) mg/mL)** \\ \hline _Pistacia lentiscus_ & 13.50 \({}^{\rm c}\) \(\pm\) 1.50 & 5.38 \({}^{\rm e}\) \(\pm\) 0.44 & 13.39 \({}^{\rm e}\) \(\pm\) 1.29 \\ _Rosmarinus officinalis_ & 12.10 \({}^{\rm d}\) \(\pm\) 0.53 & 12.25 \({}^{\rm c}\) \(\pm\) 0.24 & 20.82 \({}^{\rm b}\) \(\pm\) 0.52 \\ _Erica multiflora_ & 13.45 \({}^{\rm c}\) \(\pm\) 2.48 & 10.85 \({}^{\rm d}\) \(\pm\) 0.99 & 17.89 \({}^{\rm d}\) \(\pm\) 0.66 \\ _Calicotome villosa_ & 15.33 \({}^{\rm b}\) \(\pm\) 0.39 & 19.60 \({}^{\rm a}\) \(\pm\) 0.39 & 24.2 \({}^{\rm a}\) \(\pm\) 0.11 \\ _Phillynea latifolia_ & 20.16 \({}^{\rm a}\) \(\pm\) 0.32 & 15.59 \({}^{\rm b}\) \(\pm\) 0.66 & 19.35 \({}^{\rm c}\) \(\pm\) 0.26 \\ \hline \end{tabular} TAC, DPPH, and FRAP values showed in this table were the mean of three replicates \(\pm\) SD (n = 3). The values with different exponents in the same column (a–e) showed significant differences at \(p<0.05\).  
\end{table}
Table 1: Antioxidant activities of _Pistacia lentiscus_, _Rosmarinus officinalis_, _Erica multiflora_, _Calicotome villosa_, and _Phillynea latifolia_ ethanolic extracts.
## 3 Results and Discussion
### Physical Properties
#### 3.1.4 Capillarity Coefficient  
The variation in the capillarity absorption coefficient (CAC) of the CEBs as a function of the fiber and cement content is shown in Figure 14. It was clearly observed that all the composite materials had a CAC under 20 g/cm\({}^{2}\cdot\)min\({}^{1/2}\), which corresponds essentially to blocks with weak capillarity according to the XP P 13 901 Standard [41]. Also, it can be seen that the increase in fiber content leads to an increase in the value of CAC regardless of the cement content. This behavior can be attributed on the capacity of fibers to absorb and retain more water, and also to the fact that Juncus fibers have more pores than the soil-cement matrix. However, as the cement content increases, the CAC decreases, and its value is lower with a higher binder percentage. Therefore, the obtained results confirm that stabilized compressed earth blocks exhibit insensitivity to water and highlight their suitability for construction applications where resistance to water is crucial. These results are probably due to the capacity of cement to fill the void volume in the sample and thus reduce the number of pores between grains particles and fibers, as well as being due to the great compactness of the CEBs due to the application of 10 MPa of compaction pressure. This trends are in agreement with previous findings [25].
# Improvement of Mechanical Properties of 3D Bioprinted Structures through Cellular Overgrowth
## 2 Materials and Methods  
### Materials  
Alginic acid sodium salt from brown algae, porcine skin type B gelatin, and calcium chloride used for cross-linking hydrogel materials were purchased from Sigma Aldrich (Burlington, MA, USA). A model line of immortalized NIH/3T3 fibroblast cells from the American Type Culture Collection (ATCC, Manassas, VA, USA) was used to prepare 3D bioprinted structures. For cell cultivation, DMEM high-glucose medium (Biowest, Nuaille, France) supplemented with the iron-fortified calf serum Calf Bovine Serum (CBS) (ATCC, Manassas, VA, USA) and a mixture of penicillin-streptomycin-neomycin (P/S/N) antibiotics (Sigma Aldrich, Burlington, MA, USA) were used. A complementary PBS buffer (Capricorn Scientific GmbH, Ebsdorfergrund, Germany) was used in cell cultivation and for rinsing hydrogel printouts. The live/dead assay kit, composed of calcein-AM and ethidium homodimer III, was obtained from Biotium (Fremont, CA, USA).
###### Abstract  
As the demand for high-speed, low-latency communication continues to grow, free-space optical (FSO) communication has gained prominence as a promising solution for supporting the next generation of wireless networks, especially in the context of the 5G and beyond era. It offers high-speed, low-latency data transmission over long distances without the need for a physical infrastructure. However, the deployment of FSO systems faces significant challenges, such as atmospheric turbulence, weather-induced signal degradation, and alignment issues, all of which can impair performance. This paper offers a comprehensive survey of the enabling technologies, challenges, trends, and future prospects for FSO communication in next-generation networks, while also providing insights into the current mitigation strategies. The survey explores the critical enabling technologies such as adaptive optics, modulation schemes, and error correction codes that are revolutionizing FSO communication and addressing the unique challenges of FSO links. Also, the integration of FSO with radio frequency, millimeter-wave, and Terahertz technologies is explored, emphasizing hybrid solutions that enhance reliability and coverage. Additionally, the paper highlights emerging trends, such as the integration of FSO with artificial intelligence-driven optimization techniques and the growing role of machine learning in enhancing FSO system performance for dynamic environments. By analyzing the current trends and identifying key challenges, this paper emphasizes the prospects of FSO communication in the evolving landscape of 5G and future networks. In this regard, it assesses the potential of FSO to meet the demands for high-speed, low-latency communication and offers insights into its scalability, reliability, and deployment strategies for 5G and beyond. The paper concludes by identifying the open challenges and future research directions critical to realizing the full potential of FSO in next-generation communication systems.  
20242480362024803
# Integrated Design of Moon-to-Earth Transfer Trajectory Considering Re-Entry Constraints
## 3 Opportunity Determination with Re-Entry Constraints
### Daily Optimal Transfer Trajectory Design
Thus, the altitude of the perilune of the feasible Moon-to-Earth transfer trajectory obtained above changes approximately periodically in one day. However, the maximum altitude of the perilune can even be up to tens of thousands of kilometers. Therefore, it is necessary to find the re-entry time corresponding to the transfer trajectory with the lowest perilune altitude of lunar in one day. The change curve of the perilune altitude of the transfer trajectory with a fixed transfer duration of \(\Delta T\) in one day shows a single valley trend, so the golden section method is applied. For the optimal Moon-to-Earth transfer trajectory solution problem, the specific steps to realize the golden section method are as follows. Take points \(a_{1},a_{2}\) in the time interval \([a,b](a=D_{1},b=D_{1}+1)\) and divide the interval into three sections, among which  
\[a_{1}=a+0.382(b-a),a_{2}=a+0.618(b-a) \tag{21}\]  
Denote \(F(x)\) as the altitude of the perilune corresponding to the re-entry time of \(x\). If \(F(a_{1})<F(a_{2})\), set \(b=a_{2}\), and \(a\) remains unchanged; if \(F(a_{1})>F(a_{2})\), set \(a=a_{1}\), and \(b\) remains unchanged. The above iterative process is implemented until the optimal re-entry time accuracy is met as  
\[b-a<\epsilon_{rp} \tag{22}\]  
where \(\epsilon_{rp}\) is the error limit of the optimal re-entry time. Therefore, when the transfer duration is \(\Delta T\), the optimal re-entry time is \(T_{0}=a\). Thus, the optimal Moon-to-Earth transfer trajectory with the minimum altitude of the perilune in \([D_{1},D_{1}+1]\) is obtained. The steps for optimal Moon-to-Earth transfer trajectory design are shown as module M2 in Figure 2.
## 4 Discussion
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multirow{2}{*}{**Test**} & \multirow{2}{*}{\(t\)} & \multirow{2}{*}{\(p\)} & \multirow{2}{*}{\(\Delta\)**X**} & \multicolumn{3}{c}{**95\% CI**} & \multirow{2}{*}{**Cohen’s d**} \\ \cline{5-6}  & & & & & **Lower** & & **Upper** \\ \hline One-repetition & IT & \(-\)2.181 & 0.021 & \(-\)6.100 & \(-\)11.976 & \(-\)0.223 & \(-\)0.975 \\ \cline{2-6} maximum bench press test (BP 1 RM) & FT & 2.575 & 0.010 & 7.660 & 1.409 & 13.910 & 1.151 \\ \hline Maximum punching velocity & IT & 1.454 & 0.082 & 0.405 & \(-\)0.180 & 0.990 & 0.650 \\ \cline{2-6} (PVmax)—Rear arm (RA) & FT & 2.959 & 0.004 & 0.889 & 0.25778 & 1.52022 & 1.323 \\ \hline Maximum punching velocity & IT & 1.065 & 0.150 & 0.337 & \(-\)0.327 & 1.001 & 0.476 \\ \cline{2-6} (PVmax)—Lead arm (LA) & FT & 3.231 & 0.002 & 0.841 & 0.294 & 1.387 & 1.445 \\ \hline Yo-Yo Intermittent Recovery & IT & \(-\)0.369 & 0.358 & \(-\)0.900 & \(-\)6.021 & 4.221 & \(-\)0.165 \\ \cline{2-6} Tests—Distance & FT & 4.779 & \textless{}0.001 & 8.100 & 4.539 & 11.660 & 2.137 \\ \hline Yo-Yo Intermittent Recovery & IT & \(-\)0.218 & 0.415 & \(-\)11.000 & \(-\)116.903 & 94.903 & \(-\)0.098 \\ \cline{2-6} Tests—VO2 max & FT & 8.647 & \textless{}0.001 & 212.000 & 160.490 & 263.509 & 3.867 \\ \hline Maximal heart rate & IT & \(-\)0.217 & 0.415 & \(-\)0.092 & \(-\)0.982 & 0.798 & \(-\)0.097 \\ \cline{2-6} (HR max) & FT & 8.628 & \textless{}0.001 & 1.783 & 1.348 & 2.217 & 3.858 \\ \hline \hline \end{tabular}  
* IT—initial test, FT—final test, \(\Delta\)X—difference in means (FT-IT), SD—standard deviation, \(t\)—Student’s T value, \(p\)—level of probability, CI—confidence interval, Cohen’s d—effect size value.  
\end{table} 
Table 9: Statistical analysis of motor and functional fitness parameters—independent Student’s test between groups under study.
# A Versatile Resilience Packet Ring Protocol Model for Homogeneous Networks
## 2 Related Work
### Route Failure and Spectrum Mobility
In a physical handover, there are two types: the first one is hard, and the second one is soft. In a hard handover, first, the connection is broken with the AP, and then a new connection is established with the second AP. This is called \"break before make\" technology. In a soft handover, connections with both APs are established. This is called \"make before break\" technology.